Search

Porphyria is a group of rare metabolic disorders caused by mutations in the genes encoding crucial enzymes in the heme biosynthetic pathway. However, the lack of comprehensive genetic analysis of porphyria patients in the Chinese population makes identifying and diagnosing carriers of the condition challenging. Using the ChinaMAP database, we determined the frequencies of P/LP porphyria-associated gene variants according to the ACMG guidelines. We also calculated the carrier rates and prevalence of each type of porphyria in the Chinese population under Hardy–Weinberg equilibrium. Compared with the variants in the gnomAD database, the genetic spectrum of porphyria-related P/LP variants in the Chinese population is distinct. In the ChinaMAP database, we identified 23 variants. We estimated the carrier rates for autosomal dominant porphyrias (AIP, HCP, VP, PCT) in the Chinese population to be 1/1059, 1/1513, 1/10588, and 1/1765, respectively. For autosomal recessive porphyrias (ADP, EPP, HEP, CEP), the estimated carrier rates were 1/5294, 1/2117, 1/1765, and 1/2647, respectively, with predicted prevalence rates of 8.92 × 10−9, 7.51 × 10−5, 8.02 × 10−8, and 3.57 × 10−8, respectively. Notably, 12 of the variants we identified were unique to the Chinese population. The predicted prevalence rate of EPP was the highest among the various types of porphyria in the Chinese population, while the others were moderate to low. This is the first comprehensive genetic study on porphyria in the Chinese population. Clarifying the genetic characteristics of various porphyria types among the Chinese population provides scientifically sound reference data for both research and genetic screening to identify porphyria carriers. [ABSTRACT FROM AUTHOR]

Autism spectrum disorder (ASD) is a neurodevelopmental condition characterized by social and communication difficulties, along with repetitive behaviors. While genetic factors play a significant role in ASD, the precise genetic landscape remains complex and not fully understood, particularly in non-syndromic cases. The study performed an in silico comparison of three genetic databases. ClinVar, SFARI Gene, and AutDB were utilized to identify relevant gene subset and genetic variations associated with non-syndromic ASD. Gene set enrichment analysis (GSEA) and protein–protein interaction (PPI) network analysis were conducted to elucidate the biological significance of the identified genes. The integrity of ASD-related gene subset and the distribution of their variations were statistically assessed. A subset of twenty overlapping genes potentially specific for non-syndromic ASD was identified. GSEA revealed enrichment of biological processes related to neuronal development and differentiation, synaptic function, and social skills, highlighting their importance in ASD pathogenesis. PPI network analysis demonstrated functional relationships among the identified genes. Analysis of genetic variations showed predominance of rare variants and database-specific distribution patterns. The results provide valuable insights into the genetic landscape of ASD and outline the genes and biological processes involved in the condition, while taking into account that the study relied exclusively on in silico analyses, which may be subject to biases inherent to database methodologies. Further research incorporating multi-omics data and experimental validation is warranted to enhance our understanding of non-syndromic ASD genetics and facilitate the development of targeted research, interventions and therapies. [ABSTRACT FROM AUTHOR]

The supervised machine learning method is often used for biomedical relationship extraction. The disadvantage is that it requires much time and money to manually establish an annotated dataset. Based on distant supervision, the knowledge base is combined with the corpus, thus, the training corpus can be automatically annotated. As many biomedical databases provide knowledge bases for study with a limited number of annotated corpora, this method is practical in biomedicine. The clinical significance of each patient's genetic makeup can be understood based on the healthcare provider's genetic database. Unfortunately, the lack of previous biomedical relationship extraction studies focuses on gene–gene interaction. The main purpose of this study is to develop extraction methods for gene–gene interactions that can help explain the heritability of human complex diseases. This study referred to the information on gene–gene interactions in the KEGG PATHWAY database, the abstracts in PubMed were adopted to generate the training sample set, and the graph kernel method was adopted to extract gene–gene interactions. The best assessment result was an F1-score of 0.79. Our developed distant supervision method automatically finds sentences through the corpus without manual labeling for extracting gene–gene interactions, which can effectively reduce the time cost for manual annotation data; moreover, the relationship extraction method based on a graph kernel can be successfully applied to extract gene–gene interactions. In this way, the results of this study are expected to help achieve precision medicine. [ABSTRACT FROM AUTHOR]

Motivation: Alignment of reads to a reference genome sequence is one of the key steps in the analysis of human whole-genome sequencing data obtained through Next-generation sequencing (NGS) technologies. The quality of the subsequent steps of the analysis, such as the results of clinical interpretation of genetic variants or the results of a genome-wide association study, depends on the correct identification of the position of the read as a result of its alignment. The amount of human NGS whole-genome sequencing data is constantly growing. There are a number of human genome sequencing projects worldwide that have resulted in the creation of large-scale databases of genetic variants of sequenced human genomes. Such information about known genetic variants can be used to improve the quality of alignment at the read alignment stage when analysing sequencing data obtained for a new individual, for example, by creating a genomic graph. While existing methods for aligning reads to a linear reference genome have high alignment speed, methods for aligning reads to a genomic graph have greater accuracy in variable regions of the genome. The development of a read alignment method that takes into account known genetic variants in the linear reference sequence index allows combining the advantages of both sets of methods. Results: In this paper, we present the minimap2_index_modifier tool, which enables the construction of a modified index of a reference genome using known single nucleotide variants and insertions/deletions (indels) specific to a given human population. The use of the modified minimap2 index improves variant calling quality without modifying the bioinformatics pipeline and without significant additional computational overhead. Using the PrecisionFDA Truth Challenge V2 benchmark data (for HG002 short-read data aligned to the GRCh38 linear reference (GCA_000001405.15) with parameters k = 27 and w = 14) it was demonstrated that the number of false negative genetic variants decreased by more than 9500, and the number of false positives decreased by more than 7000 when modifying the index with genetic variants from the Human Pangenome Reference Consortium. [ABSTRACT FROM AUTHOR]

Background: Investigating novel therapeutic strategies for colorectal cancer (CRC) is imperative. However, there is limited research on the use of drugs to target peripheral blood immune cells in this context. To address this gap, we performed a two-sample Mendelian randomization (MR) analysis to identify potential therapeutic targets for CRC. Methods: We applied two-sample MR to identify the causal relationship between peripheral blood immune cells and CRC. GWAS data were obtained from the IEU OPEN GWAS project. Based on the implications from the MR results, we conducted a comprehensive database search and genetic analysis to explore potential underlying mechanisms. We predicted miRNAs for each gene and employed extensive research for potential therapeutic applications. Results: We have identified causal associations between two peripheral immune cells and colorectal cancer. Activated & resting Treg %CD4 + cell was positively associated with the risks of CRC, while DN (CD4-CD8-) %leukocyte cell exhibited a protective role in tumor progression. NEK7 (NIMA related kinase 7) and LHX9 (LIM homeobox 9) expressed in Treg cells were positively associated with CRC risks and may play a vital role in carcinogenesis. Conclusions: This study identified causal relationship between peripheral immune cell and CRC. Treg and DN T cells were implicated to own promoting and inhibiting effects on CRC progression respectively. NEK7 and LHX9 in Treg cells were identified as potential biotarget for antitumor therapies. [ABSTRACT FROM AUTHOR]

Objectives: The Bengal tiger Panthera tigris tigris, is an emblematic animal for Bangladesh. Despite being the apex predator in the wild, their number is decreasing due to anthropogenic activities such as hunting, urbanization, expansion of agriculture and deforestation. By contrast, captive tigers are flourishing due to practical conservation efforts. Breeding within the small captive population can produce inbreeding depression and genetic bottlenecks, which may limit the success of conservation efforts. Despite past decades of research, a comprehensive database on genetic variation in the captive and wild Bengal tigers in Bangladesh still needs to be included. Therefore, this research aimed to investigate the White Bengal tiger genome to create a resource for future studies to understand variation underlying important functional traits. Data description: Blood samples from Chattogram Zoo were collected for three white Bengal tigers. Genomic DNA for all collected samples were extracted using a commercial DNA extraction kit. Whole genome sequencing was performed using a DNBseq platform. We generated 77 Gb of whole-genome sequencing (WGS) data for three white Bengal tigers (Average 11X coverage/sample). The data we generated will establish a paradigm for tiger research in Bangladesh by providing a genomic resource for future functional studies on the Bengal white tiger. [ABSTRACT FROM AUTHOR]

Background: Environmental stress factors, such as biotic and abiotic stress, are becoming more common due to climate variability, significantly affecting global maize yield. Transcriptome profiling studies provide insights into the molecular mechanisms underlying stress response in maize, though the functions of many genes are still unknown. To enhance the functional annotation of maize-specific genes, MaizeGDB has outlined a data-driven approach with an emphasis on identifying genes and traits related to biotic and abiotic stress. Results: We mapped high-quality RNA-Seq expression reads from 24 different publicly available datasets (17 abiotic and seven biotic studies) generated from the B73 cultivar to the recent version of the reference genome B73 (B73v5) and deduced stress-related functional annotation of maize gene models. We conducted a robust meta-analysis of the transcriptome profiles from the datasets to identify maize loci responsive to stress, identifying 3,230 differentially expressed genes (DEGs): 2,555 DEGs regulated in response to abiotic stress, 408 DEGs regulated during biotic stress, and 267 common DEGs (co-DEGs) that overlap between abiotic and biotic stress. We discovered hub genes from network analyses, and among the hub genes of the co-DEGs we identified a putative NAC domain transcription factor superfamily protein (Zm00001eb369060) IDP275, which previously responded to herbivory and drought stress. IDP275 was up-regulated in our analysis in response to eight different abiotic and four different biotic stresses. A gene set enrichment and pathway analysis of hub genes of the co-DEGs revealed hormone-mediated signaling processes and phenylpropanoid biosynthesis pathways, respectively. Using phylostratigraphic analysis, we also demonstrated how abiotic and biotic stress genes differentially evolve to adapt to changing environments. Conclusions: These results will help facilitate the functional annotation of multiple stress response gene models and annotation in maize. Data can be accessed and downloaded at the Maize Genetics and Genomics Database (MaizeGDB). [ABSTRACT FROM AUTHOR]

This document discusses the challenges of diagnosing the causes of unexplained child deaths and presents a study on the use of molecular autopsy to identify genetic variations associated with these deaths. Traditional autopsies are not always acceptable to parents due to religious and cultural beliefs, so molecular autopsy, which involves DNA sequencing, offers a promising alternative. The study used stored neonatal dried blood spots to conduct clinical exome sequencing and found that 88.9% of cases had genetic variations associated with the cause of death. The study suggests that molecular autopsies using clinical exome sequencing can be a feasible and effective method for investigating unexplained child deaths. [Extracted from the article]

Background: The family Toxocaridae is a group of zooparasitic nematodes of veterinary, medical and economic significance. However, the evolutionary relationship of Porrocaecum and Toxocara, both genera currently classified in Toxocaridae, and the monophyly of the Toxocaridae remain under debate. Moreover, the validity of the subgenus Laymanicaecum in the genus Porrocaecum is open to question. Due to the scarcity of an available genetic database, molecular identification of Porrocaecum nematodes is still in its infancy. Methods: A number of Porrocaecum nematodes collected from the Eurasian marsh harrier Circus aeruginosus (Linnaeus) (Falconiformes: Accipitridae) in the Czech Republic were identified using integrated morphological methods (light and scanning electron microscopy) and molecular techniques (sequencing and analyzing the nuclear 18S, 28S and ITS regions). The complete mitochondrial genomes of the collected nematode specimens and of Porrocaecum (Laymanicaecum) reticulatum (Linstow, 1899) were sequenced and annotated for the first time. Phylogenetic analyses of ascaridoid nematodes based on the amino acid sequences of 12 protein-coding genes of mitochondrial genomes were performed using maximum likelihood and Bayesian inference. Results: A new species of Porrocaecum, named P. moraveci n. sp., is described based on the morphological and genetic evidence. The mitogenomes of P. moraveci n. sp. and P. reticulatum both contain 36 genes and are 14,517 and 14,210 bp in length, respectively. Comparative mitogenomics revealed that P.moraveci n. sp. represents the first known species with three non-coding regions and that P. reticulatum has the lowest overall A + T content in the mitogenomes of ascaridoid nematodes tested to date. Phylogenetic analyses showed the representatives of Toxocara clustered together with species of the family Ascarididae rather than with Porrocaecum and that P. moraveci n. sp. is a sister to P. reticulatum. Conclusions: The characterization of the complete mitochondrial genomes of P. moraveci n. sp. and P. reticulatum is reported for the first time. Mitogenomic phylogeny analyses indicated that the family Toxocaridae is non-monophyletic and that the genera Porrocaecum and Toxocara do not have an affinity. The validity of the subgenus Laymanicaecum in Porrocaecum was also rejected. Our results suggest that: (i) Toxocaridae should be degraded to a subfamily of the Ascarididae that includes only the genus Toxocara; and (ii) the subfamily Porrocaecinae should be resurrected to include only the genus Porrocaecum. The present study enriches the database of ascaridoid mitogenomes and provides a new insight into the systematics of the superfamily Ascaridoidea. [ABSTRACT FROM AUTHOR]

Background: Curated databases of genetic variants assist clinicians and researchers in interpreting genetic variation. Yet, these databases contain some misclassified variants. It is unclear whether variant misclassification is abating as these databases rapidly grow and implement new guidelines. Methods: Using archives of ClinVar and HGMD, we investigated how variant misclassification has changed over 6 years, across different ancestry groups. We considered inborn errors of metabolism (IEMs) screened in newborns as a model system because these disorders are often highly penetrant with neonatal phenotypes. We used samples from the 1000 Genomes Project (1KGP) to identify individuals with genotypes that were classified by the databases as pathogenic. Due to the rarity of IEMs, nearly all such classified pathogenic genotypes indicate likely variant misclassification in ClinVar or HGMD. Results: While the false-positive rates of both ClinVar and HGMD have improved over time, HGMD variants currently imply two orders of magnitude more affected individuals in 1KGP than ClinVar variants. We observed that African ancestry individuals have a significantly increased chance of being incorrectly indicated to be affected by a screened IEM when HGMD variants are used. However, this bias affecting genomes of African ancestry was no longer significant once common variants were removed in accordance with recent variant classification guidelines. We discovered that ClinVar variants classified as Pathogenic or Likely Pathogenic are reclassified sixfold more often than DM or DM? variants in HGMD, which has likely resulted in ClinVar's lower false-positive rate. Conclusions: Considering misclassified variants that have since been reclassified reveals our increasing understanding of rare genetic variation. We found that variant classification guidelines and allele frequency databases comprising genetically diverse samples are important factors in reclassification. We also discovered that ClinVar variants common in European and South Asian individuals were more likely to be reclassified to a lower confidence category, perhaps due to an increased chance of these variants being classified by multiple submitters. We discuss features for variant classification databases that would support their continued improvement. [ABSTRACT FROM AUTHOR]

Background: Nematodes of the family Physalopteridae (Spirurida: Physalopteroidea) commonly parasitize the alimentary canal of all major vertebrate groups. However, many physalopterid species are not adequately described, especially regarding the detailed morphology of the cephalic end. The current genetic database for Physaloptera species is still very limited, which seriously hampers molecular-based species identification. Additionally, the systematic status of some genera and the evolutionary relationships of the subfamilies in the Physalopteridae remain under debate. Methods: New morphological data for Physaloptera sibirica was gathered using light and scanning electron microscopy based on newly collected specimens from the hog badger Arctonyx collaris Cuvier (Carnivora: Mustelidae) in China. Six different genetic markers, including nuclear small ribosomal DNA (18S), large ribosomal DNA (28S) and internal transcribed spacer (ITS), mitochondrial cytochrome c oxidase subunit 1 (cox1) and subunit 2 (cox2), and the 12S small subunit ribosomal RNA gene of P. sibirica were sequenced and analyzed for the first time to our knowledge. Additionally, to construct a basic molecular phylogenetic framework for the Physalopteridae, phylogenetic analyses were performed based on the cox1 and 18S + cox1 genes using maximum likelihood (ML) and Bayesian inference (BI) methods. Results: Scanning electron microscopy (SEM) observation displayed the details of the cephalic structures, deirids, excretory pore, caudal papillae, vulva, phasmids and egg of P. sibirica for the first time to our knowledge. Pairwise comparison of the sequences obtained for P. sibirica did not reveal intraspecific divergence regarding the 18S, 28S, cox1 and 12S genetic markers and a low level of divergence in the ITS (0.16%) and cox2 (2.39%) regions. Maximum likelihood and Bayesian inference analyses showed that the representatives of Physalopteridae formed two major clades (species of Physalopterinae + Thubunaeinae parasitic in terrestrial vertebrates and Proleptinae only occurring in marine or freshwater fishes). Turgida turgida was found nested among representatives of Physaloptera. Physaloptera sibirica clustered together with P. rara. Physalopteroides sp. (Thubunaeinae) formed a sister relationship to the physalopterine Abbreviata caucasica. Conclusions: Physaloptera sibirica was redescribed, which is the fourth nematode parasite reported from the hog badger A. collaris, and A. collaris represents a new host for P. sibirica. The phylogenetic results challenged the validity of the subfamily Thubunaeinae and of the genus Turgida and supported dividing the family Physalopteridae into two subfamilies, Physalopterinae and Proleptinae. However, we do not make any immediate systematic changes in the Physalopteridae, because a more rigorous study with broader representation of the Physalopteridae is required. These present findings contribute to morphologically identifying P. sibirica more accurately and provide new insights into the systematics of the Physalopteridae. [ABSTRACT FROM AUTHOR]

Background: Population-specific variation database of inborn errors of metabolism (IEMs) is essential for precise genetic diagnosis and disease prevention. Here we presented a systematic review of clinically relevant variants of 13 IEMs genes reported among Chinese patients. Methods: A systematic search of the following electronic databases for 13 IEMs genes was conducted: PubMed-NCBI, China national knowledge infrastructure and Wanfang databases. Patient data was extracted from articles eligible for inclusion and recorded in Excel electronic form using a case-by-case approach. Results: A total of 218 articles, 93 published in English and 125 in Chinese, were retrieved. After variant annotation and deduplication, 575 unique patients (241 from articles published in Chinese) were included in the population-specific variation database. Patients identified by newborn screening and symptomatic presentation were 231 (40.17%) and 344 (59.83%), respectively. Biallelic variants were observed in 525/575 (91.3%). Among the 581 unique variants identified, 83 (14.28%) were described ≥ 3 times and 97 (16.69%) were not recorded in Clinvar or HGMD. Four variants were reclassified as benign and dozens of confusing variants deserved further research. Conclusion: This review provides a unique resource of the well-characterized diseases and causative variants that have accumulated in Chinese population and is a preliminary attempt to build the Chinese genetic variation database of IEMs. [ABSTRACT FROM AUTHOR]

Background: The extremely harsh environment of the desert is changing dramatically every moment, and the rapid adaptive stress response in the short term requires enormous energy expenditure to mobilize widespread regulatory networks, which is all the more detrimental to the survival of the desert plants themselves. The dune reed, which has adapted to desert environments with complex and variable ecological factors, is an ideal type of plant for studying the molecular mechanisms by which Gramineae plants respond to combinatorial stress of the desert in their natural state. But so far, the data on the genetic resources of reeds is still scarce, therefore most of their research has focused on ecological and physiological studies. Results: In this study, we obtained the first De novo non-redundant Full-Length Non-Chimeric (FLNC) transcriptome databases for swamp reeds (SR), dune reeds (DR) and the All of Phragmites australis (merged of iso-seq data from SR and DR), using PacBio Iso-Seq technology and combining tools such as Iso-Seq3 and Cogent. We then identified and described long non-coding RNAs (LncRNA), transcription factor (TF) and alternative splicing (AS) events in reeds based on a transcriptome database. Meanwhile, we have identified and developed for the first time a large number of candidates expressed sequence tag-SSR (EST-SSRs) markers in reeds based on UniTransModels. In addition, through differential gene expression analysis of wild-type and homogenous cultures, we found a large number of transcription factors that may be associated with desert stress tolerance in the dune reed, and revealed that members of the Lhc family have an important role in the long-term adaptation of dune reeds to desert environments. Conclusions: Our results provide a positive and usable genetic resource for Phragmites australis with a widespread adaptability and resistance, and provide a genetic database for subsequent reeds genome annotation and functional genomic studies. [ABSTRACT FROM AUTHOR]

Background: COVID-19 caused by the SARS-CoV-2 infection may result in various disease symptoms and severity, ranging from asymptomatic, through mildly symptomatic, up to very severe and even fatal cases. Although environmental, clinical, and social factors play important roles in both susceptibility to the SARS-CoV-2 infection and progress of COVID-19 disease, it is becoming evident that both pathogen and host genetic factors are important too. In this study, we report findings from whole-exome sequencing (WES) of 27 individuals who died due to COVID-19, especially focusing on frequencies of DNA variants in genes previously associated with the SARS-CoV-2 infection and the severity of COVID-19. Results: We selected the risk DNA variants/alleles or target genes using four different approaches: 1) aggregated GWAS results from the GWAS Catalog; 2) selected publications from PubMed; 3) the aggregated results of the Host Genetics Initiative database; and 4) a commercial DNA variant annotation/interpretation tool providing its own knowledgebase. We divided these variants/genes into those reported to influence the susceptibility to the SARS-CoV-2 infection and those influencing the severity of COVID-19. Based on the above, we compared the frequencies of alleles found in the fatal COVID-19 cases to the frequencies identified in two population control datasets (non-Finnish European population from the gnomAD database and genomic frequencies specific for the Slovak population from our own database). When compared to both control population datasets, our analyses indicated a trend of higher frequencies of severe COVID-19 associated risk alleles among fatal COVID-19 cases. This trend reached statistical significance specifically when using the HGI-derived variant list. We also analysed other approaches to WES data evaluation, demonstrating its utility as well as limitations. Conclusions: Although our results proved the likely involvement of host genetic factors pointed out by previous studies looking into severity of COVID-19 disease, careful considerations of the molecular-testing strategies and the evaluated genomic positions may have a strong impact on the utility of genomic testing. [ABSTRACT FROM AUTHOR]

A majority of studies reporting human genetic variants were performed in populations of European ancestry whereas other global populations, and particularly many ethnolinguistic groups in other continents, are heavily underrepresented in these studies. To investigate the extent of this disproportionate representation of global populations concerning variants of significance to thrombosis and hemostasis, 845 single nucleotide polymorphisms (SNPs) in and around 34 genes associated with thrombosis and hemostasis and included in the commercial Axiom Precision Medicine Research Array (PMRA) were evaluated, using gene frequencies in 3 African (Somali and Luhya in East Africa, and Yoruba in West Africa) and 14 non-African (admixed American, East Asian, European, South Asian, and sub-groups) populations. Among the populations studied, Europeans were observed to be the best represented population by the hemostatic SNPs included in the PMRA. The European population also presented the largest number of common pharmacogenetic and pathogenic hemostatic variants reported in the ClinVar database. The number of such variants decreased the farther the genetic distance a population was from Europeans, with Yoruba and East Asians presenting the least number of clinically significant hemostatic SNPs in ClinVar while also being the two genetically most distinct populations from Europeans among the populations compared. Current study shows the lopsided representation of global populations as regards to hemostatic genetic variants listed in different commercial SNP arrays, such as the PMRA, and reported in genetic databases while also underlining the importance of inclusion of non-European ethnolinguistic populations in genomics studies designed to discover variants of significance to bleeding and thrombotic disorders. [ABSTRACT FROM AUTHOR]

Background/objective: Arginase 1 Deficiency (ARG1-D) is a rare inherited metabolic disease with progressive, devastating neurological manifestations with early mortality and high unmet need. Information on prevalence is scarce and highly variable due to limited newborn screening (NBS) availability, variability of arginine levels in the first days of life, and high rates of misdiagnosis. US birth prevalence was recently estimated via indirect methods at 1.1 cases per million live births. Due to the autosomal recessive nature of ARG1-D we hypothesize that the global prevalence may be more accurately estimated using genetic population databases.Methods: MEDLINE and EMBASE were systematically searched for previously reported disease variants. Disease variants in ARG1-D were annotated wherever possible with allele frequencies from gnomAD. Ethnicity-specific prevalence was calculated using the Hardy-Weinberg equation and applied to generate country-specific carrier frequencies for 38 countries. Finally, documented consanguinity rates were applied to establish a birth prevalence for each country.Results: 133 of 228 (58%) known causative alleles were annotated with ethnic-specific frequencies. Global birth prevalence for ARG1-D was estimated at 2.8 cases per million live births (country-specific estimates ranged from 0.92 to 17.5) and population prevalence to be 1.4 cases per million people (approximately 1/726,000 people). Birth prevalence estimates were dependent on population demographics and consanguinity rate.Conclusion: Birth prevalence of ARG1-D based on genetic database analysis was estimated to be more frequent than previous NBS studies have indicated. There was a higher degree of confidence in North American and European countries due to availability of genetic databases and mutational analysis versus other regions. These findings suggest the need for greater disease education around signs and manifestations of ARG1-D, as well as more widespread testing and standardization of screening for this severe disease in order to appropriately identify patients prior to disease progression. [ABSTRACT FROM AUTHOR]

Background: Malaria continues to be a major public health problem in the Northeastern part of India despite the implementation of vector control measures and changes in drug policies. To develop successful vaccines against malaria, it is important to assess the diversity of vaccine candidate antigens in field isolates. This study was done to assess the diversity of Plasmodium falciparum AMA-1 vaccine candidate antigen in a malaria-endemic region of Tripura in Northeast India and compare it with previously reported global isolates with a view to assess the feasibility of developing a universal vaccine based on this antigen. Methods: Patients with fever and malaria-like illness were screened for malaria and P. falciparum positive cases were recruited for the current study. The diversity of PfAMA-1 vaccine candidate antigen was evaluated by nested PCR and RFLP. A selected number of samples were sequenced using the Sanger technique. Results: Among 56 P. falciparum positive isolates, Pfama-1 was successfully amplified in 75% (n = 42) isolates. Allele frequencies of PfAMA-1 antigen were 16.6% (n = 7) for 3D7 allele and 33.3% (n = 14) in both K1 and HB3 alleles. DNA sequencing revealed 13 haplotypes in the Pfama-1 gene including three unique haplotypes not reported earlier. No unique amino-acid substitutions were found. Global analysis with 2761 sequences revealed 435 haplotypes with a very complex network composition and few clusters. Nucleotide diversity for Tripura (0.02582 ± 0.00160) showed concordance with South-East Asian isolates while recombination parameter (Rm = 8) was lower than previous reports from India. Population genetic structure showed moderate differentiation. Conclusions: Besides documenting all previously reported allelic forms of the vaccine candidate PfAMA-1 antigen of P. falciparum, new haplotypes not reported earlier, were found in Tripura. Neutrality tests indicate that the Pfama-1 population in Tripura is under balancing selection. This is consistent with global patterns. However, the high haplotype diversity observed in the global Pfama-1 network analysis indicates that designing a universal vaccine based on this antigen may be difficult. This information adds to the existing database of genetic diversity of field isolates of P. falciparum and may be helpful in the development of more effective vaccines against the parasite. [ABSTRACT FROM AUTHOR]

Objective: To determine the effect of polymorphisms and mutations in angiotensin-converting enzyme 2 (ACE2) and Type 2 transmembrane serine proteases (TMPRSS2) genes on susceptibility to corona virus disease 2019 (COVID-19) and patient prognosis. Introduction: From December 2019 to the current time, an outbreak of epidemic of COVID-19, characterized by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) has occurred around the world. It is now clear that SARS-CoV-2 binds to human ACE2 receptors, with expression of these receptors correlated with the rate of SARS-CoV-2 infection and mortality. Polymorphisms in individual patient factors, such as ACE2 and TMPRSS2 genes have been linked with an increase in negative outcomes, although evidence to affirm remains debatable. Methods: Here, we performed a systematic review, based on guidelines of the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) criteria, with the aim of assessing whether polymorphisms in ACE2 and TMPRSS2 genes affect the COVID-19 condition. We extensively searched PubMed, MEDLINE, Embase, the Cochrane Library, and Web of Science databases, for relevant articles and reports published in English between December 2019 and December 2021. Results: A total of 495 full-text articles were downloaded, of which 185 were excluded after preliminary examination as they were duplicates. Finally, 310 articles were evaluated, by reading their titles and abstracts, and 208 of them eliminated based on our selection criteria. Finally, 33 articles met our inclusion criteria and were included in the final assessment. Genetic data from 33,923 patients with COVID-19 drawn from the general population and deriving from over 160 regions and 50 countries, as well as approximately 560,000 samples from global-public genetic databases, were included in our analysis. Ultimately, we identified 10 SNPs and 21 mutations in the ACE2 gene, along with 13 SNPs and 12 variants in the TMPRSS2 gene, which may be associated with COVID-19. Conclusions: ACE2 and TMPRSS2 play vital roles in the onset, development, and prognosis of SARS-CoV-2 infection, and have both been strongly associated with vulnerability, intensity, and the clinical result of COVID-19. Overall, these genetic factors may have potential for future development of personalized drugs and vaccines against COVID-19. Trial registration: CRD42021239400 in PROSPERO 2021. [ABSTRACT FROM AUTHOR]

Atrial fibrillation (AF) is a morbid and heritable irregular cardiac rhythm that affects about 2%-3% of the population. Patients with early-onset AF have a strong genetic association with the disease; nonetheless, the exact underlying mechanisms need clarification. We herein present our evaluation of a 2-generation Iranian pedigree with early-onset AF. Whole-exome sequencing was applied to elucidate the genetic predisposition. Direct DNA sequencing was utilized to confirm and screen the variants in the proband and his available family members. The pathogenicity of the identified nucleotide variations was scrutinized via either segregation analysis in the family or in silico predictive software. The comprehensive variant analysis revealed a missense variant (c.G681C, p.E227D, rs1477078144) in the human α-dystrobrevin gene (DTNA), which is rare in genetic databases. Most in silico analyses have predicted this variant as a disease-causing variant, and the variant is co-segregated with the disease phenotype in the family. Previous studies have demonstrated the association between the DTNA gene and left ventricular noncompaction cardiomyopathy. Taken together, we provide the first evidence of an association between a nucleotide variation in the DTNA gene and early-onset AF in an Iranian family. However, the genetic testing of AF in the Iranian population is still limited. This finding not only further confirms the significant role of genetics in the incidence of early-onset AF but also expands the spectrum of the gene variations that lead to AF. Additionally, it may have further implications for the treatment and prevention of AF. [ABSTRACT FROM AUTHOR]

Objective: We present a genetic analysis of an asymptomatic family with a 4q terminal deletion; we also review other similar published studies and discuss the genotype–phenotype correlation. Methods: A karyotype analysis was performed on the amniotic fluid cells of a woman at 24 weeks of pregnancy and peripheral blood lymphocytes from both parents and their older son with the conventional G-banding technique. Chromosomal microarray analysis (CMA) testing was carried out for both parents and the fetus to analyze copy number variation (CNV) in the whole genome. Results: The results showed no abnormalities in the karyotypes of the father and older son, and the karyotypes of the mother and fetus were 46,XX,del(4)(q35.1) and 46,XY,del(4)(q35.1), respectively. CMA results showed a partial deletion at the 4q terminus in both the fetus and mother. The deletion region of the fetus was arr[GRCh37] 4q35.1q35.2(186,431,008_190,957,460) × 1; the loss size of the CNV was approximately 4.5 Mb and involved 14 protein-coding genes, namely, CYP4V2, F11, FAM149A, FAT1, FRG1, FRG2, KLKB1, MTNR1A, PDLIM3, SORBS2, TLR3, TRIML1, TRIML2, and ZFP42. No variation on chromosome 4 was detected in the father's CMA results. Conclusion: Deletion of the 4q subtelomeric region is a familial variation. The arr[GRCh37] 4q35.1q35.2(186,431,008_190,957,460) region single-copy deletion did not cause obvious congenital defects or mental retardation. The application of high-resolution genetic testing technology combined with the analysis of public genetic database information can more clearly elucidate the genotype–phenotype correlation of the disease and provide support for both prenatal and postnatal genetic counseling. [ABSTRACT FROM AUTHOR]

Background: One of the major challenges in precision medicine is accurate prediction of individual patient's response to drugs. A great number of computational methods have been developed to predict compounds activity using genomic profiles or chemical structures, but more exploration is yet to be done to combine genetic mutation, gene expression, and cheminformatics in one machine learning model. Results: We presented here a novel deep-learning model that integrates gene expression, genetic mutation, and chemical structure of compounds in a multi-task convolutional architecture. We applied our model to the Genomics of Drug Sensitivity in Cancer (GDSC) and Cancer Cell Line Encyclopedia (CCLE) datasets. We selected relevant cancer-related genes based on oncology genetics database and L1000 landmark genes, and used their expression and mutations as genomic features in model training. We obtain the cheminformatics features for compounds from PubChem or ChEMBL. Our finding is that combining gene expression, genetic mutation, and cheminformatics features greatly enhances the predictive performance. Conclusion: We implemented an extended Graph Neural Network for molecular graphs and Convolutional Neural Network for gene features. With the employment of multi-tasking and self-attention functions to monitor the similarity between compounds, our model outperforms recently published methods using the same training and testing datasets. [ABSTRACT FROM AUTHOR]

Background: Dyschromatosis universalis hereditaria (DUH) is a pigmentary dermatosis characterized by generalized mottled macules with hypopigmention and hyperpigmention. ABCB6 and SASH1 are recently reported pathogenic genes related to DUH, and the aim of this study was to identify the causative mutations in a Chinese family with DUH. Methods: Sanger sequencing was performed to investigate the clinical manifestation and molecular genetic basis of these familial cases of DUH, bioinformatics tools and multiple sequence alignment were used to analyse the pathogenicity of mutations. Results: A novel missense mutation, c.1529G>A, in the SASH1 gene was identified, and this mutation was not found in the National Center for Biotechnology Information Database of Short Genetic Variation, Online Mendelian Inheritance in Man, ClinVar, or 1000 Genomes Project databases. All in silico predictors suggested that the observed substitution mutation was deleterious. Furthermore, multiple sequence alignment of SASH1 revealed that the p.S510N mutation was highly conserved during evolution. In addition, we reviewed the previously reported DUH-related gene mutations in SASH1 and ABCB6. Conclusion: Although the affected family members had identical mutations, differences in the clinical manifestations of these family members were observed, which reveals the complexity of the phenotype-influencing factors in DUH. Our findings reveal the mutation responsible for DUH in this family and broaden the mutational spectrum of the SASH1 gene. [ABSTRACT FROM AUTHOR]

Background: Breast cancer is one of the leading causes of cancer-related deaths in women, and there is a demand in developing an Asian-based genetic profiling database for breast cancer in improving the treatment response. This study aimed to determine molecular alternations and identify potential therapeutic targets by analyzing the genetic profiling from a cohort of Taiwanese breast cancers using a commercialized next-generation sequencing (NGS) targeted panel.Methods: The study population comprised a broad spectrum of breast cancer patients in Taiwan, including Group 1: planned to receive first-line surgery and followed by adjuvant therapy, or early relapse within three years, Group 2: planned to receive first-line neoadjuvant therapy and followed by surgery, and Group 3: de novo stage IV, or stage IV with recurrence beyond three years. Molecular profiles were determined using Thermo Fisher™ Oncomine™ Comprehensive Assay version 3 (TMO comprehensive assay) from Formalin-Fixed Paraffin-Embedded (FFPE) tissues. Level of actionability was evaluated with the ESMO Scale of clinical actionability of molecular targets (ESCAT).Results: A total of 380 TMO comprehensive assays were conducted on 372 patients, and we presented targeted sequencing analyses of Tier I: alteration-drug match associated with improved outcome in clinical trials including ERBB2 amplification, BRCA1/2 germline mutation, PIK3CA mutation, and NTRK translocation, and Tier II: antitumor activity associated with the matched alteration-drug but lack of prospective outcome data including PTEN loss, ESR1 mutation, AKT1 mutation, and ERBB2 mutation, and Tier III: matched drug-alteration that led to clinical benefit in another tumor type including MDM2 amplification, and ERBB3 mutation. Among them, 249 (66%) showed at least one actionable alternation based on the ESCAT criteria. The most frequent impacted genes (all variants combined within each sample) were PIK3CA (38%), followed by ERBB2 (23%), ESR1 (10%), AKT1 (6%), and BRCA2 (5%), and the remaining rare variants (less than 5% of assayed cohort) were BRCA1 (3%), MDM2 (2.2%), and ERBB3 (1.1%).Conclusion: Targeted sequencing of actionable genes is believed to provide clinical applicability and substantial benefits for Taiwanese breast cancer patients. A valid scale of clinical actionability should be adopted for precision medicine practice under multidisciplinary molecular tumor board. [ABSTRACT FROM AUTHOR]

Background: Finding long matches in deoxyribonucleic acid (DNA) sequences in large aligned genetic sequences is a problem of great interest. A paradigmatic application is the identification of distant relatives via large common subsequences in DNA data. However, because of the sensitive nature of genomic data such computations without security consideration might compromise the privacy of the individuals involved. Methods: The secret sharing technique enables the computation of matches while respecting the privacy of the inputs of the parties involved. This method requires interaction that depends on the circuit depth needed for the computation. Results: We design a new depth-optimized algorithm for computing set-maximal matches between a database of aligned genetic sequences and the DNA of an individual while respecting the privacy of both the database owner and the individual. We then implement and evaluate our protocol. Conclusions: Using modern cryptographic techniques, difficult genomic computations are performed in a privacy-preserving way. We enrich this research area by proposing a privacy-preserving protocol for set-maximal matches. [ABSTRACT FROM AUTHOR]

Background: Whole-exome sequencing (WES) has become an efficient diagnostic test for patients with likely monogenic conditions such as rare idiopathic diseases or sudden unexplained death. Yet, many cases remain undiagnosed. Here, we report the added diagnostic yield achieved for 101 WES cases re-analyzed 1 to 7 years after initial analysis. Methods: Of the 101 WES cases, 51 were rare idiopathic disease cases and 50 were postmortem "molecular autopsy" cases of early sudden unexplained death. Variants considered for reporting were prioritized and classified into three groups: (1) diagnostic variants, pathogenic and likely pathogenic variants in genes known to cause the phenotype of interest; (2) possibly diagnostic variants, possibly pathogenic variants in genes known to cause the phenotype of interest or pathogenic variants in genes possibly causing the phenotype of interest; and (3) variants of uncertain diagnostic significance, potentially deleterious variants in genes possibly causing the phenotype of interest. Results: Initial analysis revealed diagnostic variants in 13 rare disease cases (25.4%) and 5 sudden death cases (10%). Re-analysis resulted in the identification of additional diagnostic variants in 3 rare disease cases (5.9%) and 1 sudden unexplained death case (2%), which increased our molecular diagnostic yield to 31.4% and 12%, respectively. Conclusions: The basis of new findings ranged from improvement in variant classification tools, updated genetic databases, and updated clinical phenotypes. Our findings highlight the potential for re-analysis to reveal diagnostic variants in cases that remain undiagnosed after initial WES. [ABSTRACT FROM AUTHOR]

Background: Gene Set Enrichment Analysis (GSEA) is a powerful tool to identify enriched functional categories of informative biomarkers. Canonical GSEA takes one-dimensional feature scores derived from the data of one platform as inputs. Numerous extensions of GSEA handling multimodal OMIC data are proposed, yet none of them explicitly captures combinatorial relations of feature scores from multiple platforms. Results: We propose multivariate GSEA (MGSEA) to capture combinatorial relations of gene set enrichment among multiple platform features. MGSEA successfully captures designed feature relations from simulated data. By applying it to the scores of delineating breast cancer and glioblastoma multiforme (GBM) subtypes from The Cancer Genome Atlas (TCGA) datasets of CNV, DNA methylation and mRNA expressions, we find that breast cancer and GBM data yield both similar and distinct outcomes. Among the enriched functional categories, subtype-specific biomarkers are dominated by mRNA expression in many functional categories in both cancer types and also by CNV in many functional categories in breast cancer. The enriched functional categories belonging to distinct combinatorial patterns are involved different oncogenic processes: cell proliferation (such as cell cycle control, estrogen responses, MYC and E2F targets) for mRNA expression in breast cancer, invasion and metastasis (such as cell adhesion and epithelial-mesenchymal transition (EMT)) for CNV in breast cancer, and diverse processes (such as immune and inflammatory responses, cell adhesion, angiogenesis, and EMT) for mRNA expression in GBM. These observations persist in two external datasets (Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) for breast cancer and Repository for Molecular Brain Neoplasia Data (REMBRANDT) for GBM) and are consistent with knowledge of cancer subtypes. We further compare the characteristics of MGSEA with several extensions of GSEA and point out the pros and cons of each method. Conclusions: We demonstrated the utility of MGSEA by inferring the combinatorial relations of multiple platforms for cancer subtype delineation in three multi-OMIC datasets: TCGA, METABRIC and REMBRANDT. The inferred combinatorial patterns are consistent with the current knowledge and also reveal novel insights about cancer subtypes. MGSEA can be further applied to any genotype-phenotype association problems with multimodal OMIC data. [ABSTRACT FROM AUTHOR]

Background: One of the most important and often neglected components of a successful RNA sequencing (RNA-Seq) experiment is sample size estimation. A few negative binomial model-based methods have been developed to estimate sample size based on the parameters of a single gene. However, thousands of genes are quantified and tested for differential expression simultaneously in RNA-Seq experiments. Thus, additional issues should be carefully addressed, including the false discovery rate for multiple statistic tests, widely distributed read counts and dispersions for different genes. Results: To solve these issues, we developed a sample size and power estimation method named RnaSeqSampleSize, based on the distributions of gene average read counts and dispersions estimated from real RNA-seq data. Datasets from previous, similar experiments such as the Cancer Genome Atlas (TCGA) can be used as a point of reference. Read counts and their dispersions were estimated from the reference's distribution; using that information, we estimated and summarized the power and sample size. RnaSeqSampleSize is implemented in R language and can be installed from Bioconductor website. A user friendly web graphic interface is provided at http://cqs.mc.vanderbilt.edu/shiny/RnaSeqSampleSize/. Conclusions: RnaSeqSampleSize provides a convenient and powerful way for power and sample size estimation for an RNAseq experiment. It is also equipped with several unique features, including estimation for interested genes or pathway, power curve visualization, and parameter optimization. [ABSTRACT FROM AUTHOR]

Background: Genomic islands (GIs) are inserts of foreign DNA that have potentially arisen through horizontal gene transfer (HGT). There are evidences that GIs can contribute significantly to the evolution of prokaryotes. The acquisition of GIs through HGT in eukaryotes has, however, been largely unexplored. In this study, the previously developed GI prediction tool, SeqWord Gene Island Sniffer (SWGIS), is modified to predict GIs in eukaryotic chromosomes. Artificial simulations are used to estimate ratios of predicting false positive and false negative GIs by inserting GIs into different test chromosomes and performing the SWGIS v2.0 algorithm. Using SWGIS v2.0, GIs are then identified in 36 fungal, 22 protozoan and 8 invertebrate genomes. Results: SWGIS v2.0 predicts GIs in large eukaryotic chromosomes based on the atypical nucleotide composition of these regions. Averages for predicting false negative and false positive GIs were 20.1% and 11.01% respectively. A total of 10,550 GIs were identified in 66 eukaryotic species with 5299 of these GIs coding for at least one functional protein. The EuGI web-resource, freely accessible at http://eugi.bi.up.ac.za, was developed that allows browsing the database created from identified GIs and genes within GIs through an interactive and visual interface. Conclusions: SWGIS v2.0 along with the EuGI database, which houses GIs identified in 66 different eukaryotic species, and the EuGI web-resource, provide the first comprehensive resource for studying HGT in eukaryotes. [ABSTRACT FROM AUTHOR]

Background: Large sequence datasets are difficult to visualize and handle. Additionally, they often do not represent a random subset of the natural diversity, but the result of uncoordinated and convenience sampling. Consequently, they can suffer from redundancy and sampling biases. Results: Here we present Treemmer, a simple tool to evaluate the redundancy of phylogenetic trees and reduce their complexity by eliminating leaves that contribute the least to the tree diversity. Conclusions: Treemmer can reduce the size of datasets with different phylogenetic structures and levels of redundancy while maintaining a sub-sample that is representative of the original diversity. Additionally, it is possible to fine-tune the behavior of Treemmer including any kind of meta-information, making Treemmer particularly useful for empirical studies. [ABSTRACT FROM AUTHOR]

Background: Detailed modelling of the neutral mutational process in cancer cells is crucial for identifying driver mutations and understanding the mutational mechanisms that act during cancer development. The neutral mutational process is very complex: whole-genome analyses have revealed that the mutation rate differs between cancer types, between patients and along the genome depending on the genetic and epigenetic context. Therefore, methods that predict the number of different types of mutations in regions or specific genomic elements must consider local genomic explanatory variables. A major drawback of most methods is the need to average the explanatory variables across the entire region or genomic element. This procedure is particularly problematic if the explanatory variable varies dramatically in the element under consideration. Results: To take into account the fine scale of the explanatory variables, we model the probabilities of different types of mutations for each position in the genome by multinomial logistic regression. We analyse 505 cancer genomes from 14 different cancer types and compare the performance in predicting mutation rate for both regional based models and site-specific models. We show that for 1000 randomly selected genomic positions, the site-specific model predicts the mutation rate much better than regional based models. We use a forward selection procedure to identify the most important explanatory variables. The procedure identifies site-specific conservation (phyloP), replication timing, and expression level as the best predictors for the mutation rate. Finally, our model confirms and quantifies certain well-known mutational signatures. Conclusion: We find that our site-specific multinomial regression model outperforms the regional based models. The possibility of including genomic variables on different scales and patient specific variables makes it a versatile framework for studying different mutational mechanisms. Our model can serve as the neutral null model for the mutational process; regions that deviate from the null model are candidates for elements that drive cancer development. [ABSTRACT FROM AUTHOR]

Background: Recent breakthroughs in molecular biology and next generation sequencing technologies have led to the expenential growh of the sequence databases. Researchrs use BLAST for processing these sequences. However traditional software parallelization techniques (threads, message passing interface) applied in newer versios of BLAST are not adequate for processing these sequences in timely manner. Methods: A new method for array job parallelization has been developed which offers O(T) theoretical speed-up in comparison to multi-threading and MPI techniques. Here T is the number of array job tasks. (The number of CPUs that will be used to complete the job equals the product of T multiplied by the number of CPUs used by a single task.) The approach is based on segmentation of both input datasets to the BLAST process, combining partial solutions published earlier (Dhanker and Gupta, Int J Comput Sci Inf Technol_5:4818-4820, 2014), (Grant et al., Bioinformatics_18:765-766, 2002), (Mathog, Bioinformatics_19:1865-1866, 2003). It is accordingly referred to as a "dual segmentation" method. In order to implement the new method, the BLAST source code was modified to allow the researcher to pass to the program the number of records (effective number of sequences) in the original database. The team also developed methods to manage and consolidate the large number of partial results that get produced. Dual segmentation allows for massive parallelization, which lifts the scaling ceiling in exciting ways. Results: BLAST jobs that hitherto failed or slogged inefficiently to completion now finish with speeds that characteristically reduce wallclock time from 27 days on 40 CPUs to a single day using 4104 tasks, each task utilizing eight CPUs and taking less than 7 minutes to complete. Conclusions: The massive increase in the number of tasks when running an analysis job with dual segmentation reduces the size, scope and execution time of each task. Besides significant speed of completion, additional benefits include fine-grained checkpointing and increased flexibility of job submission. "Trickling in" a swarm of individual small tasks tempers competition for CPU time in the shared HPC environment, and jobs submitted during quiet periods can complete in extraordinarily short time frames. The smaller task size also allows the use of older and less powerful hardware. The CDRH workhorse cluster was commissioned in 2010, yet its eight-core CPUs with only 24GB RAM work well in 2017 for these dual segmentation jobs. Finally, these techniques are excitingly friendly to budget conscious scientific research organizations where probabilistic algorithms such as BLAST might discourage attempts at greater certainty because single runs represent a major resource drain. If a job that used to take 24 days can now be completed in less than an hour or on a space available basis (which is the case at CDRH), repeated runs for more exhaustive analyses can be usefully contemplated. [ABSTRACT FROM AUTHOR]

Background: In the search for novel causal mutations, public and/or private variant databases are nearly always used to facilitate the search as they result in a massive reduction of putative variants in one step. Practically, variant filtering is often done by either using all variants from the variant database (called the absence-approach, i.e. it is assumed that disease-causing variants do not reside in variant databases) or by using the subset of variants with an allelic frequency > 1% (called the 1%-approach). We investigate the validity of these two approaches in terms of false negatives (the true disease-causing variant does not pass all filters) and false positives (a harmless mutation passes all filters and is erroneously retained in the list of putative disease-causing variants) and compare it with an novel approach which we named the quantile-based approach. This approach applies variable instead of static frequency thresholds and the calculation of these thresholds is based on prior knowledge of disease prevalence, inheritance models, database size and database characteristics. Results: Based on real-life data, we demonstrate that the quantile-based approach outperforms the absence-approach in terms of false negatives. At the same time, this quantile-based approach deals more appropriately with the variable allele frequencies of disease-causing alleles in variant databases relative to the 1%-approach and as such allows a better control of the number of false positives. We also introduce an alternative application for variant database usage and the quantile-based approach. If disease-causing variants in variant databases deviate substantially from theoretical expectancies calculated with the quantile-based approach, their association between genotype and phenotype had to be reconsidered in 12 out of 13 cases. Conclusions: We developed a novel method and demonstrated that this so-called quantile-based approach is a highly suitable method for variant filtering. In addition, the quantile-based approach can also be used for variant flagging. For user friendliness, lookup tables and easy-to-use R calculators are provided. [ABSTRACT FROM AUTHOR]

Background: Sparse principal component analysis (PCA) is a popular tool for dimensionality reduction, pattern recognition, and visualization of high dimensional data. It has been recognized that complex biological mechanisms occur through concerted relationships of multiple genes working in networks that are often represented by graphs. Recent work has shown that incorporating such biological information improves feature selection and prediction performance in regression analysis, but there has been limited work on extending this approach to PCA. In this article, we propose two new sparse PCA methods called Fused and Grouped sparse PCA that enable incorporation of prior biological information in variable selection. Results: Our simulation studies suggest that, compared to existing sparse PCA methods, the proposed methods achieve higher sensitivity and specificity when the graph structure is correctly specified, and are fairly robust to misspecified graph structures. Application to a glioblastoma gene expression dataset identified pathways that are suggested in the literature to be related with glioblastoma. Conclusions: The proposed sparse PCA methods Fused and Grouped sparse PCA can effectively incorporate prior biological information in variable selection, leading to improved feature selection and more interpretable principal component loadings and potentially providing insights on molecular underpinnings of complex diseases. [ABSTRACT FROM AUTHOR]

Background: Gene co-expression, the similarity of gene expression profiles under various experimental conditions, has been used as an indicator of functional relationships between genes, and many co-expression databases have been developed for predicting gene functions. These databases usually provide users with a co-expression network and a list of strongly co-expressed genes for a query gene. Several of these databases also provide functional information on a set of strongly co-expressed genes (i.e., provide biological processes and pathways that are enriched in these strongly co-expressed genes), which is generally analyzed via over-representation analysis (ORA). A limitation of this approach may be that users can predict gene functions only based on the strongly co-expressed genes. Results: In this study, we developed a new co-expression database that enables users to predict the function of tomato genes from the results of functional enrichment analyses of co-expressed genes while considering the genes that are not strongly co-expressed. To achieve this, we used the ORA approach with several thresholds to select co-expressed genes, and performed gene set enrichment analysis (GSEA) applied to a ranked list of genes ordered by the co-expression degree. We found that internal correlation in pathways affected the significance levels of the enrichment analyses. Therefore, we introduced a new measure for evaluating the relationship between the gene and pathway, termed the percentile (p)-score, which enables users to predict functionally relevant pathways without being affected by the internal correlation in pathways. In addition, we evaluated our approaches using receiver operating characteristic curves, which concluded that the p-score could improve the performance of the ORA. Conclusions: We developed a new database, named Co-expressed Pathways DataBase for Tomato, which is available at http://cox-path-db.kazusa.or.jp/tomato. The database allows users to predict pathways that are relevant to a query gene, which would help to infer gene functions. [ABSTRACT FROM AUTHOR]

Background: Enterocytozoon bieneusi is the dominant specie of microsporidia which can infect both anthroponotic and zoonotic species. The golden snub-nosed monkey is an endangered primate which can also infect by E. bieneusi. To date, few genetic data on E. bieneusi from golden snub-nosed monkeys has been published. Therefore, to clarify the prevalence and genotypes of E. bieneusi in captive golden snub-nosed monkeys is necessary to assess the potential for zoonotic transmission. Result: We examined 160 golden snub-nosed monkeys from six zoos in four cities in China, using PCR and comparative sequence analysis of the ribosomal internal transcribed spacer (ITS). The overall prevalence of E. bieneusi was 46.2% (74/160); while the prevalence was 26.7%, 69.1%, 69.4% and 33.3% in Shanghai Zoo, Shanghai Wild Animal Park, Tongling Zoo, and Taiyuan Zoo respectively (P = 0.006). A total of seven E. bieneusi genotypes were found that included four known (D, J, CHG1, and CHG14) and three new (CM19-CM 21) genotypes. The most common genotype was D (54/74, 73.0%), followed by J (14/74, 18.9%); other genotypes were restricted to one or two samples. Phylogenetic analysis revealed that genotype D belonged to the previously-characterized Group 1, with zoonotic potential; whereas genotypes J, CHG1, CHG14 and CM19-CM 21 clustered in the previously-characterized Group 2, the so-called cattle host specificity group. Conclusions: The findings of high prevalence of zoonotic E. bieneusi genotypes D and J in golden snub-nosed monkeys suggest that golden snub-nosed monkeys may be the reservoir hosts for human microsporidiosis, and vice versa. [ABSTRACT FROM AUTHOR]

Background: Paspalum notatum Flügge is a subtropical grass native to South America, which includes sexual diploid and apomictic polyploid biotypes. In the past decade, a number of apomixis-associated genes were discovered in this species through genetic mapping and differential expression surveys. However, the scarce information on Paspalum sequences available in public databanks limited annotations and functional predictions for these candidates. Results: We used a long-read 454/Roche FLX+ sequencing strategy to produce robust reference transcriptome datasets from florets of sexual and apomictic Paspalum notatum genotypes and delivered a list of transcripts showing differential representation in both reproductive types. Raw data originated from floral samples collected from premeiosis to anthesis was assembled in three libraries: i) sexual (SEX), ii) apomictic (APO) and iii) global (SEX + APO). A group of physicallysupported Paspalum mRNA and EST sequences matched with high level of confidence to both sexual and apomictic libraries. A preliminary trial allowed discovery of the whole set of putative alleles/paralogs corresponding to 23 previously identified apomixis-associated candidate genes. Moreover, a list of 3,732 transcripts and several co-expression and protein-protein interaction networks associated with apomixis were identified. Conclusions: The use of the 454/Roche FLX+ transcriptome database will allow the detailed characterization of floral alleles/paralogs of apomixis candidate genes identified in prior and future work. Moreover, it was used to reveal additional candidate genes differentially represented in apomictic and sexual flowers. Gene ontology (GO) analyses of this set of transcripts indicated that the main molecular pathways altered in the apomictic genotype correspond to specific biological processes, like biotic and abiotic stress responses, growth, development, cell death and senescence. This data collection will be of interest to the plant reproduction research community and, particularly, to Paspalum breeding projects. [ABSTRACT FROM AUTHOR]

Background: Population structure inference using the software STRUCTURE has become an integral part of population genetic studies covering a broad spectrum of taxa including humans. The ever-expanding size of genetic data sets poses computational challenges for this analysis. Although at least one tool currently implements parallel computing to reduce computational overload of this analysis, it does not fully automate the use of replicate STRUCTURE analysis runs required for downstream inference of optimal K. There is pressing need for a tool that can deploy population structure analysis on high performance computing clusters. Results: We present an updated version of the popular Python program StrAuto, to streamline population structure analysis using parallel computing. StrAuto implements a pipeline that combines STRUCTURE analysis with the Evanno ΔK analysis and visualization of results using STRUCTURE HARVESTER. Using benchmarking tests, we demonstrate that StrAuto significantly reduces the computational time needed to perform iterative STRUCTURE analysis by distributing runs over two or more processors. Conclusion: StrAuto is the first tool to integrate STRUCTURE analysis with post-processing using a pipeline approach in addition to implementing parallel computation - a set up ideal for deployment on computing clusters. StrAuto is distributed under the GNU GPL (General Public License) and available to download from http://strauto.popgen.org. [ABSTRACT FROM AUTHOR]

Background: RNAs play key roles in cells through the interactions with proteins known as the RNA-binding proteins (RBP) and their binding motifs enable crucial understanding of the post-transcriptional regulation of RNAs. How the RBPs correctly recognize the target RNAs and why they bind specific positions is still far from clear. Machine learning-based algorithms are widely acknowledged to be capable of speeding up this process. Although many automatic tools have been developed to predict the RNA-protein binding sites from the rapidly growing multi-resource data, e.g. sequence, structure, their domain specific features and formats have posed significant computational challenges. One of current difficulties is that the cross-source shared common knowledge is at a higher abstraction level beyond the observed data, resulting in a low efficiency of direct integration of observed data across domains. The other difficulty is how to interpret the prediction results. Existing approaches tend to terminate after outputting the potential discrete binding sites on the sequences, but how to assemble them into the meaningful binding motifs is a topic worth of further investigation. Results: In viewing of these challenges, we propose a deep learning-based framework (iDeep) by using a novel hybrid convolutional neural network and deep belief network to predict the RBP interaction sites and motifs on RNAs. This new protocol is featured by transforming the original observed data into a high-level abstraction feature space using multiple layers of learning blocks, where the shared representations across different domains are integrated. To validate our iDeep method, we performed experiments on 31 large-scale CLIP-seq datasets, and our results show that by integrating multiple sources of data, the average AUC can be improved by 8% compared to the best single-source-based predictor; and through cross-domain knowledge integration at an abstraction level, it outperforms the state-of-the-art predictors by 6%. Besides the overall enhanced prediction performance, the convolutional neural network module embedded in iDeep is also able to automatically capture the interpretable binding motifs for RBPs. Large-scale experiments demonstrate that these mined binding motifs agree well with the experimentally verified results, suggesting iDeep is a promising approach in the real-world applications. Conclusion: The iDeep framework not only can achieve promising performance than the state-of-the-art predictors, but also easily capture interpretable binding motifs. iDeep is available at http://www.csbio.sjtu.edu.cn/bioinf/iDeep [ABSTRACT FROM AUTHOR]

Background: Deriving valid variant calling results from raw next-generation sequencing data is a particularly challenging task, especially with respect to clinical diagnostics and personalized medicine. However, when using classic variant calling software, the user usually obtains nothing more than a list of variants that pass the corresponding caller's internal filters. Any expected mutations (e.g. hotspot mutations), that have not been called by the software, need to be investigated manually. Results: BBCAnalyzer (Bases By CIGAR Analyzer) provides a novel visual approach to facilitate this step of timeconsuming, manual inspection of common mutation sites. BBCAnalyzer is able to visualize base counts at predefined positions or regions in any sequence alignment data that are available as BAM files. Thereby, the tool provides a straightforward solution for evaluating any list of expected mutations like hotspot mutations, or even whole regions of interest. In addition to an ordinary textual report, BBCAnalyzer reports highly customizable plots. Information on the counted number of bases, the reference bases, known mutations or polymorphisms, called mutations and base qualities is summarized in a single plot. By uniting this information in a graphical way, the user may easily decide on a variant being present or not - completely independent of any internal filters or frequency thresholds. Conclusions: BBCAnalyzer provides a unique, novel approach to facilitate variant calling where classical tools frequently fail to call. The R package is freely available at http://bioconductor.org. The local web application is available at Additional file 2. A documentation of the R package (Additional file 1) as well as the web application (Additional file 2) with detailed descriptions, examples of all input-and output elements, exemplary code as well as exemplary data are included. A video demonstrates the exemplary usage of the local web application (Additional file 3). [ABSTRACT FROM AUTHOR]

Background: Stochastic mapping is frequently used in comparative biology to simulate character evolution, enabling the probabilistic computation of statistics such as number of state transitions along a tree and distribution of states in its internal nodes. Common implementations rely on Continuous-time Markov Chain simulations whose parameters are difficult to adjust and subjected to inherent inaccuracy. Thus, researchers must run a large number of simulations in order to obtain adequate estimates. Although execution time tends to be relatively small when simulations are performed on a single tree assumed to be the "true" topology, it may become an issue if analyses are conducted on several trees, such as the ones that make up posterior distributions obtained via Bayesian phylogenetic inference. Working with such distributions is preferable to working with a single tree, for they allow the integration of phylogenetic uncertainty into parameter estimation. In such cases, detailed character mapping becomes less important than parameter integration across topologies. Here, we present an R-based implementation (SFREEMAP) of an analytical approach to obtain accurate, per-branch expectations of numbers of state transitions and dwelling times. We also introduce an intuitive way of visualizing the results by integrating over the posterior distribution and summarizing the parameters onto a target reference topology (such as a consensus or MAP tree) provided by the user. Results: We benchmarked SFREEMAP's performance against make.simmap, a popular R-based implementation of stochastic mapping. SFREEMAP confirmed theoretical expectations outperforming make.simmap in every experiment and reducing computation time of relatively modest datasets from hours to minutes. We have also demonstrated that SFREEMAP returns estimates which were not only similar to the ones obtained by averaging across make.simmap mappings, but also more accurate, according to simulated data. We illustrate our visualization strategy using previously published data on the evolution of coloniality in scleractinian corals. Conclusion: SFREEMAP is an accurate and fast alternative to ancestral state reconstruction via simulation-based stochastic mapping. [ABSTRACT FROM AUTHOR]

Background: In the last years, applications based on massively parallelized RNA sequencing (RNA-seq) have become valuable approaches for studying non-model species, e.g., without a fully sequenced genome. RNA-seq is a useful tool for detecting novel transcripts and genetic variations and for evaluating differential gene expression by digital measurements. The large and complex datasets resulting from functional genomic experiments represent a challenge in data processing, management, and analysis. This problem is especially significant for small research groups working with non-model species. Results: We developed a web-based application, called ATGC transcriptomics, with a flexible and adaptable interface that allows users to work with new generation sequencing (NGS) transcriptomic analysis results using an ontology-driven database. This new application simplifies data exploration, visualization, and integration for a better comprehension of the results. Conclusions: ATGC transcriptomics provides access to non-expert computer users and small research groups to a scalable storage option and simple data integration, including database administration and management. The software is freely available under the terms of GNU public license at http://atgcinta.sourceforge.net. [ABSTRACT FROM AUTHOR]

Background: Molecular genetic markers are one of the most informative and widely used genome features in clinical and environmental diagnostic studies. A polymerase chain reaction (PCR)-based molecular marker is very attractive because it is suitable to high throughput automation and confers high specificity. However, the design of taxon-specific primers may be difficult and time consuming due to the need to identify appropriate genomic regions for annealing primers and to evaluate primer specificity. Results: Here, we report the development of a Tool for Identification of Primers for Multiple Taxa (TipMT), which is a web application to search and design primers for genotyping based on genomic data. The tool identifies and targets single sequence repeats (SSR) or orthologous/taxa-specific genes for genotyping using Multiplex PCR. This pipeline was applied to the genomes of four species of Leishmania (L. amazonensis, L. braziliensis, L. infantum and L. major) and validated by PCR using artificial genomic DNA mixtures of the Leishmania species as templates. This experimental validation demonstrates the reliability of TipMT because amplification profiles showed discrimination of genomic DNA samples from Leishmania species. Conclusions: The TipMT web tool allows for large-scale identification and design of taxon-specific primers and is freely available to the scientific community at http://200.131.37.155/tipMT/. [ABSTRACT FROM AUTHOR]

Background: Differences in chromatin states are critical to the multiplicity of cell states. Recently genome-wide histone modification maps of diverse human developmental stages and tissues have been charted. Description: To facilitate the investigation of epigenetic dynamics and regulatory mechanisms in cellular differentiation processes, we developed iHMS, an integrated human histone modification database that incorporates massive histone modification maps spanning different developmental stages, lineages and tissues (http://www.tongjidmb.com/human/index.html). It also includes genome-wide expression data of different conditions, reference gene annotations, GC content and CpG island information. By providing an intuitive and user-friendly query interface, iHMS enables comprehensive query and comparative analysis based on gene names, genomic region locations, histone modification marks and cell types. Moreover, it offers an efficient browser that allows users to visualize and compare multiple genome-wide histone modification maps and related expression profiles across different developmental stages and tissues. Conclusion: iHMS is of great helpfulness to understand how global histone modification state transitions impact cellular phenotypes across different developmental stages and tissues in the human genome. This extensive catalog of histone modification states thus presents an important resource for epigenetic and developmental studies. [ABSTRACT FROM AUTHOR]

Background: Transcription factors (TFs) often interact with one another to form TF complexes that bind DNA and regulate gene expression. Many databases are created to describe known TF complexes identified by either mammalian two-hybrid experiments or data mining. Lately, a wealth of ChIP-seq data on human TFs under different experiment conditions are available, making it possible to investigate condition-specific (cell type and/or physiologic state) TF complexes and their target genes. Results: Here, we developed a systematic pipeline to infer Condition-Specific Targets of human TF-TF complexes (called the CST pipeline) by integrating ChIP-seq data and TF motifs. In total, we predicted 2,392 TF complexes and 13,504 high-confidence or 127,994 low-confidence regulatory interactions amongst TF complexes and their target genes. We validated our predictions by (i) comparing predicted TF complexes to external TF complex databases, (ii) validating selected target genes of TF complexes using ChIP-qPCR and RT-PCR experiments, and (iii) analysing target genes of select TF complexes using gene ontology enrichment to demonstrate the accuracy of our work. Finally, the predicted results above were integrated and employed to construct a CST database. Conclusions: We built up a methodology to construct the CST database, which contributes to the analysis of transcriptional regulation and the identification of novel TF-TF complex formation in a certain condition. This database also allows users to visualize condition-specific TF regulatory networks through a user-friendly web interface. [ABSTRACT FROM AUTHOR]

Background: Next-generation sequencing (NGS) informs many biological questions with unprecedented depth and nucleotide resolution. These assays have created a need for analytical tools that enable users to manipulate data nucleotide-by-nucleotide robustly and easily. Furthermore, because many NGS assays encode information jointly within multiple properties of read alignments - for example, in ribosome profiling, the locations of ribosomes are jointly encoded in alignment coordinates and length - analytical tools are often required to extract the biological meaning from the alignments before analysis. Many assay-specific pipelines exist for this purpose, but there remains a need for user-friendly, generalized, nucleotide-resolution tools that are not limited to specific experimental regimes or analytical workflows. Results: Plastid is a Python library designed specifically for nucleotide-resolution analysis of genomics and NGS data. As such, Plastid is designed to extract assay-specific information from read alignments while retaining generality and extensibility to novel NGS assays. Plastid represents NGS and other biological data as arrays of values associated with genomic or transcriptomic positions, and contains configurable tools to convert data from a variety of sources to such arrays. Plastid also includes numerous tools to manipulate even discontinuous genomic features, such as spliced transcripts, with nucleotide precision. Plastid automatically handles conversion between genomic and featurecentric coordinates, accounting for splicing and strand, freeing users of burdensome accounting. Finally, Plastid's data models use consistent and familiar biological idioms, enabling even beginners to develop sophisticated analytical workflows with minimal effort. Conclusions: Plastid is a versatile toolkit that has been used to analyze data from multiple NGS assays, including RNA-seq, ribosome profiling, and DMS-seq. It forms the genomic engine of our ORF annotation tool, ORF-RATER, and is readily adapted to novel NGS assays. Examples, tutorials, and extensive documentation can be found at https://plastid.readthedocs.io. [ABSTRACT FROM AUTHOR]

Background: One of the most challenging issue in the variant calling process is handling the resulting data, and filtering the genes retaining only the ones strictly related to the topic of interest. Several tools permit to gather annotations at different levels of complexity for the detected genes and to group them according to the pathways and/or processes they belong to. However, it might be a time consuming and frustrating task. This is partly due to the size of the file, that might contain many thousands of genes, and to the search of associated variants that requires a gene-by-gene investigation and annotation approach. As a consequence, the initial gene list is often reduced exploiting the knowledge of variants effect, novelty and genotype, with the potential risk of losing meaningful pieces of information. Results: Here we present Var2GO, a new web-based tool to support the annotation and filtering of variants and genes coming from variant calling of high-throughput sequencing data. Var2GO permits to upload either the unprocessed Variant Calling Format file or a table containing the annotated variants. The raw data undergo a preliminary step of variants annotation, using the SnpEff tool, and are converted to a table format. The table is then uploaded into an on the fly generated database. Genes associated to the variants are automatically annotated with the corresponding Gene Ontology terms covering the three GO domains. Using the web interface it is then possible to filter and extract, from the whole list, genes having annotations in the domain of interest, by simply specifying filtering parameters and one or more keywords. The relevance of this tool is demonstrated on exome sequencing data. Conclusions: Var2GO is a novel tool that implements a topic-based approach, expressly designed to help biologists in narrowing the search of relevant genes coming from variant calling analysis. Its main purpose is to support non-bioinformaticians in handling and processing raw variant calling data through an intuitive web interface. Furthermore, Var2GO offers a complete pipeline that, starting from the raw VCF file, allows to annotate both variants and associated genes and supports the extraction of relevant biological knowledge. [ABSTRACT FROM AUTHOR]

Background: Network-based integrative analysis is a powerful technique for extracting biological insights from multilayered omics data such as somatic mutations, copy number variations, and gene expression data. However, integrated analysis of multi-omics data is quite complicated and can hardly be done in an automated way. Thus, a powerful interactive visual mining tool supporting diverse analysis algorithms for identification of driver genes and regulatory modules is much needed. Results: Here, we present a software platform that integrates network visualization with omics data analysis tools seamlessly. The visualization unit supports various options for displaying multi-omics data as well as unique network models for describing sophisticated biological networks such as complex biomolecular reactions. In addition, we implemented diverse in-house algorithms for network analysis including network clustering and over-representation analysis. Novel functions include facile definition and optimized visualization of subgroups, comparison of a series of data sets in an identical network by data-to-visual mapping and subsequent overlaying function, and management of custom interaction networks. Utility of MONGKIE for network-based visual data mining of multi-omics data was demonstrated by analysis of the TCGA glioblastoma data. MONGKIE was developed in Java based on the NetBeans plugin architecture, thus being OS-independent with intrinsic support of module extension by third-party developers. Conclusion: We believe that MONGKIE would be a valuable addition to network analysis software by supporting many unique features and visualization options, especially for analysing multi-omics data sets in cancer and other diseases. [ABSTRACT FROM AUTHOR]

Background: Identifying subpopulations within a study and inferring intercontinental ancestry of the samples are important steps in genome wide association studies. Two software packages are widely used in analysis of substructure: Structure and Eigenstrat. Structure assigns each individual to a population by using a Bayesian method with multiple tuning parameters. It requires considerable computational time when dealing with thousands of samples and lacks the ability to create scores that could be used as covariates. Eigenstrat uses a principal component analysis method to model all sources of sampling variation. However, it does not readily provide information directly relevant to ancestral origin; the eigenvectors generated by Eigenstrat are sample specific and thus cannot be generalized to other individuals. Results: We developed FastPop, an efficient R package that fills the gap between Structure and Eigenstrat. It can: 1, generate PCA scores that identify ancestral origins and can be used for multiple studies; 2, infer ancestry information for data arising from two or more intercontinental origins. We demonstrate the use of FastPop using 2318 SNP markers selected from the genome based on high variability among European, Asian and West African (African) populations. We conducted an analysis of 505 Hapmap samples with European, African or Asian ancestry along with 19661 additional samples of unknown ancestry. The results from FastPop are highly consistent with those obtained by Structure across the 19661 samples we studied. The correlations of the results between FastPop and Structure are 0.99, 0.97 and 0.99 for European, African and Asian ancestry scores, respectively. Compared with Structure, FastPop is more efficient as it finished ancestry inference for 19661 samples in 16 min compared with 21-24 h required by Structure. FastPop also provided scores based on SNP weights so the scores of reference population can be applied to other studies provided the same set of markers are used. We also present application of the method for studying four continental populations (European, Asian, African, and Native American). Conclusions: We developed an algorithm that can infer ancestries on data involving two or more intercontinental origins. It is efficient for analyzing large datasets. Additionally the PCA derived scores can be applied to multiple data sets to ensure the same ancestry analysis is applied to all studies. [ABSTRACT FROM AUTHOR]

Background: Introduction of vector species into new areas represents a main driver for the emergence and worldwide spread of vector-borne diseases. This poses a substantial threat to livestock economies and public health. Culicoides imicola Kieffer, a major vector species of economically important animal viruses, is described with an apparent range expansion in Europe where it has been recorded in south-eastern continental France, its known northern distribution edge. This questioned on further C. imicola population extension and establishment into new territories. Studying the spatio-temporal genetic variation of expanding populations can provide valuable information for the design of reliable models of future spread. Methods: Entomological surveys and population genetic approaches were used to assess the spatio-temporal population dynamics of C. imicola in France. Entomological surveys (2-3 consecutive years) were used to evaluate population abundances and local spread in continental France (28 sites in the Var department) and in Corsica (4 sites). We also genotyped at nine microsatellite loci insects from 3 locations in the Var department over 3 years (2008, 2010 and 2012) and from 6 locations in Corsica over 4 years (2002, 2008, 2010 and 2012). Results: Entomological surveys confirmed the establishment of C. imicola populations in Var department, but indicated low abundances and no apparent expansion there within the studied period. Higher population abundances were recorded in Corsica. Our genetic data suggested the absence of spatio-temporal genetic changes within each region but a significant increase of the genetic differentiation between Corsican and Var populations through time. The lack of intra-region population structure may result from strong gene flow among populations. We discussed the observed temporal variation between Corsica and Var as being the result of genetic drift following introduction, and/or the genetic characteristics of populations at their range edge. Conclusions: Our results suggest that local range expansion of C. imicola in continental France may be slowed by the low population abundances and unsuitable climatic and environmental conditions. [ABSTRACT FROM AUTHOR]

Current cytogenetics has largely focused its efforts on the identification of recurrent karyotypic alterations, also known as clonal chromosomal aberrations (CCAs). The rationale of doing so seems simple: recurrent genetic changes are relevant for diseases or specific physiological conditions, while non clonal chromosome aberrations (NCCAs) are insignificant genetic background or noise. However, in reality, the vast majority of chromosomal alterations are NCCAs, and it is challenging to identify commonly shared CCAs in most solid tumors. Furthermore, the karyotype, rather than genes, represents the system inheritance, or blueprint, and each NCCA represents an altered genome system. These realizations underscore the importance of the re-evaluation of NCCAs in cytogenetic analyses. In this concept article, we briefly review the definition of NCCAs, some historical misconceptions about them, and why NCCAs are not insignificant "noise," but rather a highly significant feature of the cellular population for providing genome heterogeneity and complexity, representing one important form of fuzzy inheritance. The frequencies of NCCAs also represent an index to measure both internally- and environmentally-induced genome instability. Additionally, the NCCA/CCA cycle is associated with macro- and micro-cellular evolution. Lastly, elevated NCCAs are observed in many disease/illness conditions. Considering all of these factors, we call for the immediate action of studying and reporting NCCAs. Specifically, effort is needed to characterize and compare different types of NCCAs, to define their baseline in various tissues, to develop methods to access mitotic cells, to re-examine/ interpret the NCCAs data, and to develop an NCCA database. [ABSTRACT FROM AUTHOR]