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Mouse A9 cells, deficient in hypoxanthine phosphoribosyltransferase (EC 2.4.2.8), were fused with normal chick erythrocytes and selected in hypoxanthine-aminopterin-thymidine medium for cells with hypoxanthine phosphoribosyltransferase activity. Recovered hybrid cells produced the chick hypoxanthine phosphoribosyltransferase exclusively, as demonstrated by electrophoretic mobility and immunoprecipitation tests, even though no chick chromosomes or chick cell-surface antigens could be identified in the hybrids. Surprisingly, the expression of the chick hypoxanthine phosphoribosyl-transferase activity in the mouse/chick hybrids required the presence of aminopterin in the growth medium; in its absence, enzyme synthesis decreased markedly. Because of the rapid and reversible modulation of hypoxanthine phosphoribosyltransferase activity, the hybrid cells could proliferate equally well in media containing hypoxanthine-aminopterin-thymidine or 8-azaguanine. Cellular selection was definitely ruled out as a possible cause. These results confirm previous reports that specific genetic information can be selectively transferred from one cell to another of a distant species. Furthermore, they demonstrate that an avian gene, whose activity is normally expressed constitutively, can become facultative when integrated into a mammalian cell. This seems to be the first instance where heterologous gene activity has been shown to be reversibly modulated in hybrid cells.

Rearrangement and in most cases expression of the T cell rearranging genes gamma (TRG gamma) and T cell antigen receptor beta chain (TCR beta) genes were studied in 19 cases of T cell acute malignancies where the surface phenotype is representative of the different stages of thymic maturation. TCR alpha gene transcription was also studied. TRG gamma and TCR beta genes were found to be rearranged in all but one case. The TRG gamma rearrangement pattern seen in most cases is compatible with biallelic rearrangement by loop excision involving the J gamma 2 regions. The sizes of all but two rearranged bands were identical to those of the rearranged bands seen in polyclonal T lymphocytes also studied in this work. One identical-sized band was found in 11 of the 18 rearranged cases. The expression of TRG gamma mRNA (transcripts of 1.6 kilobases [kb]) was highly variable from case to case and did not correlate with the stage of differentiation of the malignant cells, the expression of the molecules CD4 and CD8, the expression and size of the transcripts of the TCR beta genes, and the transcription of TCR alpha genes. In one CD3 + case, strong expression of the TRG gamma transcripts coexisted with the exclusive presence of TCR beta mRNA of 1.0 kb. The cells from this case did not react with anti-Ti antibody and exhibited no natural killer activity. These findings are suggestive of a malignancy that may express the recently isolated CD3-TRG gamma complex.

Histidine auxotrophs of Micrococcus luteus strain ATCC 27141 were induced by treatment of the parent strain with N-methyl-N'-nitro-N-nitrosoguanidine. Auxotrophs were biochemically characterized by examining culture accumulations of histidine intermediates, using paper chromatography and the Bratton-Marshall test, and growth responses to L-histidinol. his(IG) mutants failed to accumulate Pauly-positive imidazoles; his(EAHF) mutants accumulated 5-amino-1-ribosyn-4-imidazole carboxamide; hisB mutants accumulated imidazoleglycerol; hisC mutants accumulated imidazoleacetol; hisD mutants, but did stimulate all other histidine mutants, blocked at earlier steps in the biosynthetic pathway. In addition, imidazoleglycerol phosphate dehydrase activity was assayed in representative mutants of each class. hisB mutants lacked activity for this enzyme.--Two -point, three-point, and cotransformation analyses resolved linkage relationships of histidine genes and in two gene clusters aided in determining their sequences. Histidine biosynthetic genes exist in at least four separate, unlinked regions of the chromosome. One histidine gene cluster linked to a tryptophan gene cluster and appears to be contiguous in the sequence his(IG) -his(EAHF)-trpE-trpC-trpA. A second and unlinked histidine cluster has the tentative gene sequence his(EAHF)-hisB-hisC-his(EAHF). The hisD gene and an unclassified mutant site his-94 are not linked to any of the other histidine genes examined in this study or to each other.

Previous genetic analysis indicated that at least two genes determine the ultraviolet (UV) sensitivity of Bacillus subtilis spores. The present study shows that these genes independently control two distinguishable processes for removing UV-induced spore photoproduct (5-thyminyl-5,6-dihydrothymine, or TDHT) from spore deoxyribonucleic acid. The first, is a spore repair mechanism by which TDHT is removed rapidly without appearing in acid-soluble form. This mechanism, which is demonstrated in both UV-resistant and excision-deficient strains, operates to a certain extent during germination without requiring vegetative growth. The second, demonstrated in a mutant which lacks the first mechanism, removes TDHT relatively slowly and only if germinated spores are allowed to develop toward vegetative cells. The latter mechanism appears identical to excision-resynthesis repair, since the mutation abolishing it renders the irradiated vegetative cells incapable of removing cyclobutane-type pyrimidine dimers. Blocking either one of these mechanisms only slightly affects the UV sensitivity of spores, but blocking both prevents TDHT removal and gives high UV sensitivity.

A pair of frame shift mutations in the lysozyme gene of bacteriophage T4 results in the substitution of a glutamyl-tyrosyl sequence for the asparagine residue that is the penultimate amino-terminal amino acid in the lysozyme of the wild-type strain. One of the mutations has been identified as the insertion of two bases, the other as the insertion of a single base.

Three indole analogues, 5-methylindole, 5-fluoroindole, and 7-methylindole, and the tryptophan analogue 5-fluorotryptophan were found to inhibit the growth of wild-type Pseudomonas putida. Mutants resistant to these analogues were obtained. Some of the 5-fluoroindole- and 5-fluorotryptophan-resistant strains exhibit an abnormality in the regulation of certain trp genes. These strains excrete anthranilate when grown in minimal medium in the presence or absence of the inhibitor. In these strains, the trpA, B, and D gene products, the first, second, and fourth enzymes of the tryptophan pathway, are produced in 20-fold excess over the normal wild-type level. The other enzymes of the pathway are unaffected. Exogenous tryptophan is still able to repress the expression of the trpABD cluster somewhat. Similarity between the 5-fluoroindole- and 5-fluorotryptophan-resistant strains suggests that the former compound becomes effective through conversion to the latter. Repression and derepression experiments with two anthranilate-excreting, 5-fluoroindole-resistant strains showed coordinate variation of the affected enzymes. The locus conferring resistance and excretion is not linked by transduction to any of the trp genes.

Sensitive assays of histidase activity were used to follow the production of this enzyme as directed by a gene newly introduced into cells of Bacillus subtilis by transformation. Histidase activity can be detected in histidase-negative recipient cells within 1 hr after the addition of deoxyribonucleic acid extracted from histidase-positive donors. Enzyme production continues for one to two additional hours and then ceases. Histidase production in the transformed cells is fully sensitive to catabolite repression. Catabolite repression is rapidly established after transformation of recipient cells that are resistant to this form of regulation.

Two strains with mutations in the first structural gene of the histidine operon of Salmonella typhimurium were characterized. (The first structural gene specifies the first enzyme of histidine biosynthesis, phosphoribosyltransferase, which is sensitive to feedback inhibition by histidine.) One mutation, hisG3934, results in a phosphoribosyltransferase which is no longer sensitive to feedback inhibition by histidine but is instead subject to inhibition by aspartic acid. The other mutation, hisG3935, allows the histidine operon to be partially repressed by several amino acids, including aspartic acid. Analysis of hisG3935 is consistent with the hypothesis that phosphoribosyltransferase is directly involved in the regulation of the histidine operon.

Background: Backfat thickness is an important carcass composition trait for pork production and is commonly included in swine breeding programmes. In this paper, we report the results of a large genome-wide association study for backfat thickness using data from eight lines of diverse genetic backgrounds., Methods: Data comprised 275,590 pigs from eight lines with diverse genetic backgrounds (breeds included Large White, Landrace, Pietrain, Hampshire, Duroc, and synthetic lines) genotyped and imputed for 71,324 single-nucleotide polymorphisms (SNPs). For each line, we estimated SNP associations using a univariate linear mixed model that accounted for genomic relationships. SNPs with significant associations were identified using a threshold of p < 10 -6 and used to define genomic regions of interest. The proportion of genetic variance explained by a genomic region was estimated using a ridge regression model., Results: We found significant associations with backfat thickness for 264 SNPs across 27 genomic regions. Six genomic regions were detected in three or more lines. The average estimate of the SNP-based heritability was 0.48, with estimates by line ranging from 0.30 to 0.58. The genomic regions jointly explained from 3.2 to 19.5% of the additive genetic variance of backfat thickness within a line. Individual genomic regions explained up to 8.0% of the additive genetic variance of backfat thickness within a line. Some of these 27 genomic regions also explained up to 1.6% of the additive genetic variance in lines for which the genomic region was not statistically significant. We identified 64 candidate genes with annotated functions that can be related to fat metabolism, including well-studied genes such as MC4R, IGF2, and LEPR, and more novel candidate genes such as DHCR7, FGF23, MEDAG, DGKI, and PTN., Conclusions: Our results confirm the polygenic architecture of backfat thickness and the role of genes involved in energy homeostasis, adipogenesis, fatty acid metabolism, and insulin signalling pathways for fat deposition in pigs. The results also suggest that several less well-understood metabolic pathways contribute to backfat development, such as those of phosphate, calcium, and vitamin D homeostasis., (© 2021. The Author(s).)

Non-coding RNA (ncRNA) genes assume increasing biological importance, with growing associations with diseases. Many ncRNA sources are transcript-centric, but for non-coding variant analysis and disease decipherment it is essential to transform this information into a comprehensive set of genome-mapped ncRNA genes. We present GeneCaRNA, a new all-inclusive gene-centric ncRNA database within the GeneCards Suite. GeneCaRNA information is integrated from four community-backed data structures: the major transcript database RNAcentral with its 20 encompassed databases, and the ncRNA entries of three major gene resources HGNC, Ensembl and NCBI Gene. GeneCaRNA presents 219,587 ncRNA gene pages, a 7-fold increase from those available in our three gene mining sources. Each ncRNA gene has wide-ranging annotation, mined from >100 worldwide sources, providing a powerful GeneCards-leveraged search. The latter empowers VarElect, our disease-gene interpretation tool, allowing one to systematically decipher ncRNA variants. The combined power of GeneCaRNA with GeneHancer, our regulatory elements database, facilitates wide-ranging scrutiny of the non-coding terra incognita of gene networks and whole genome analyses., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2021 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Background: Exploring the relationship between disease and gene is of great significance for understanding the pathogenesis of disease and developing corresponding therapeutic measures. The prediction of disease-gene association by computational methods accelerates the process., Results: Many existing methods cannot fully utilize the multi-dimensional biological entity relationship to predict disease-gene association due to multi-source heterogeneous data. This paper proposes FactorHNE, a factor graph-aggregated heterogeneous network embedding method for disease-gene association prediction, which captures a variety of semantic relationships between the heterogeneous nodes by factorization. It produces different semantic factor graphs and effectively aggregates a variety of semantic relationships, by using end-to-end multi-perspectives loss function to optimize model. Then it produces good nodes embedding to prediction disease-gene association., Conclusions: Experimental verification and analysis show FactorHNE has better performance and scalability than the existing models. It also has good interpretability and can be extended to large-scale biomedical network data analysis.

Thousands of pathway diagrams are published each year as static figures inaccessible to computational queries and analyses. Using a combination of machine learning, optical character recognition, and manual curation, we identified 64,643 pathway figures published between 1995 and 2019 and extracted 1,112,551 instances of human genes, comprising 13,464 unique NCBI genes, participating in a wide variety of biological processes. This collection represents an order of magnitude more genes than found in the text of the same papers, and thousands of genes missing from other pathway databases, thus presenting new opportunities for discovery and research.

Cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC) is the fourth commonest female malignancy worldwide. CESC progresses in immune-microenvironment mainly composed of infiltrating immune and stromal cells. Here, we performed an integrated analysis incorporating the expression profiles from the Cancer Genome Atlas (TCGA) database and scores of immune and stromal cells calculated by Estimation of Stromal and Immune cells in Malignant Tumours using Expression data (ESTIMATE) algorithm. A two-gene signature (CD1C and CD6 genes) was established to predict the prognosis of CESC. Based on this signature, patients were divided into the high- and low-risk groups, and this signature showed good prognostic performance according to the results of Kaplan-Meier analysis and receiver operating characteristic (ROC) analysis in train set and two validation sets. A nomogram was built for evaluating the clinical applicability of this signature. In addition, based on Tumor Immune Estimation Resource (TIMER) database, 2 hub genes showed negative correlations with tumor purity and positive correlations with infiltrating levels of immune filtrating cells. What's more, we propose new treatment strategies for the two prognostic subtypes. Low- risk patients were found presenting with a higher level of immune checkpoint molecules and showing higher immunogenicity in immunophenoscore (IPS) analysis, which indicated a better response for immunotherapy. Meanwhile, estimated by Genomics of Drug Sensitivity in Cancer (GDSC) database, the high-risk patients showed sensitive responses to five chemotherapy drugs. Finally, 10 candidate small-molecule drugs for CESC were defined. In summary, the CD1C-CD6 signature can accurately predict the prognosis of CESC., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2020 Elsevier B.V. All rights reserved.)

At present, bioinformatics research focuses on the development from the accumulation of biological data to the integration and processing of biological data. This paper designs a bio gene information collection system based on data mining technology. In the system, the information of gene web analysis database, data mining model database and gene chip database is transferred to gene algorithm tool library, which can extract, transform and load the biological gene information, transfer the collected and processed biological gene information to gene general chip and web database analysis logic, and pass it to gene expression spectrum chip/data mining module through API function GUI, through the data mining module GUI feedback to the system users. The system hardware stores the biochip information in the virtual chip set model through the gene expression spectrum data analysis model uses the gene expression similarity analysis model to analyze the expression similarity of the biological gene information, and stores the information in the gene chip database; through the multi-layer structure model, constructs the web genome biochip including the application layer, the data processing layer and the representation layer. The information analysis module analyzes the biological gene information and stores the information in the gene web analysis database. The system software adopts the method of automatic collection of biological gene data based on the web to realize the collection of biological gene information, and gives the main implementation technology of the system. The experimental results show that the system can effectively collect biological gene information, and has high accuracy and anti-noise performance.

Gene Ontology (GO) is a controlled vocabulary of terms that describe molecule function, biological roles, and cellular locations of gene products (i.e., proteins and RNAs), it hierarchically organizes more than 43,000 GO terms via the direct acyclic graph. A gene is generally annotated with several of these GO terms. Therefore, accurately predicting the association between genes and massive terms is a difficult challenge. To combat with this challenge, we propose an matrix factorization based approach called NMFGO. NMFGO stores the available GO annotations of genes in a gene-term association matrix and adopts an ontological structure based taxonomic similarity measure to capture the GO hierarchy. Next, it factorizes the association matrix into two low-rank matrices via nonnegative matrix factorization regularized with the GO hierarchy. After that, it employs a semantic similarity based k nearest neighbor classifier in the low-rank matrices approximated subspace to predict gene functions. Empirical study on three model species (S. cerevisiae, H. sapiens, and A. thaliana) shows that NMFGO is robust to the input parameters and achieves significantly better prediction performance than GIC, TO, dRW- kNN, and NtN, which were re-implemented based on the instructions of the original papers. The supplementary file and demo codes of NMFGO are available at http://mlda.swu.edu.cn/codes.php?name=NMFGO.

Background: Gene selection is one of the critical steps in the course of the classification of microarray data. Since particle swarm optimization has no complicated evolutionary operators and fewer parameters need to be adjusted, it has been used increasingly as an effective technique for gene selection. Since particle swarm optimization is apt to converge to local minima which lead to premature convergence, some particle swarm optimization based gene selection methods may select non-optimal genes with high probability. To select predictive genes with low redundancy as well as not filtering out key genes is still a challenge., Results: To obtain predictive genes with lower redundancy as well as overcome the deficiencies of traditional particle swarm optimization based gene selection methods, a hybrid gene selection method based on gene scoring strategy and improved particle swarm optimization is proposed in this paper. To select the genes highly related to out samples' classes, a gene scoring strategy based on randomization and extreme learning machine is proposed to filter much irrelevant genes. With the third-level gene pool established by multiple filter strategy, an improved particle swarm optimization is proposed to perform gene selection. In the improved particle swarm optimization, to decrease the likelihood of the premature of the swarm the Metropolis criterion of simulated annealing algorithm is introduced to update the particles, and the half of the swarm are reinitialized when the swarm is trapped into local minima., Conclusions: Combining the gene scoring strategy with the improved particle swarm optimization, the new method could select functional gene subsets which are significantly sensitive to the samples' classes. With the few discriminative genes selected by the proposed method, extreme learning machine and support vector machine classifiers achieve much high prediction accuracy on several public microarray data, which in turn verifies the efficiency and effectiveness of the proposed gene selection method.

Over the past half-century, there have been concerted efforts to standardize how clinicians and medical researchers refer to genetic material. However, practical and historical impediments thwart this goal. In the current paper I argue that the ontological status of a genetic mutation cannot be cleanly separated from its pragmatic role in therapy. Attempts at standardization fail due to the non-standardized ends to which genetic information is employed, along with historical inertia and unregulated local innovation. These factors prevent rationalistic attempts to 'modernize' what is otherwise trumpeted as the most modern of the medical sciences.

In this paper, we provide an overview of our ongoing project in the Genetics and Human Agency Initiative sponsored by the John Templeton Foundation. Our project focuses on the ways that lay beliefs about the heritability of virtue influence reasoning about the nature of virtue, parenting behaviors, and the development of virtue in children. First, we provide philosophical perspectives on the nature of virtue and suggest that viewing virtue as a malleable skill may have important advantages. Next, we review theory and research that highlights the ways that lay heritability beliefs potentially undermine conceptualizations of virtue as a malleable skill. Finally, we discuss how lay heritability beliefs might ultimately affect parent-child interactions and child virtue development. The paper thus provides a brief description our project's theoretical foundation and a general look at the empirical questions it will tackle.

Selecting functional genes is essential for analyzing microarray data. Among many available feature (gene) selection approaches, the ones on the basis of the large margin nearest neighbor receive more attention due to their low computational costs and high accuracies in analyzing the high-dimensional data. Yet, there still exist some problems that hamper the existing approaches in sifting real target genes, including selecting erroneous nearest neighbors, high sensitivity to irrelevant genes, and inappropriate evaluation criteria. Previous pioneer works have partly addressed some of the problems, but none of them are capable of solving these problems simultaneously. In this paper, we propose a new local-nearest-neighbors-based feature weighting approach to alleviate the above problems. The proposed approach is based on the trick of locally minimizing the within-class distances and maximizing the between-class distances with the nearest neighbors rule. We further define a feature weight vector, and construct it by minimizing the cost function with a regularization term. The proposed approach can be applied naturally to the multi-class problems and does not require extra modification. Experimental results on the UCI and the open microarray data sets validate the effectiveness and efficiency of the new approach.

With the rapid development of DNA microarray technology, large amount of genomic data has been generated. Classification of these microarray data is a challenge task since gene expression data are often with thousands of genes but a small number of samples. In this paper, an effective gene selection method is proposed to select the best subset of genes for microarray data with the irrelevant and redundant genes removed. Compared with original data, the selected gene subset can benefit the classification task. We formulate the gene selection task as a manifold regularized subspace learning problem. In detail, a projection matrix is used to project the original high dimensional microarray data into a lower dimensional subspace, with the constraint that the original genes can be well represented by the selected genes. Meanwhile, the local manifold structure of original data is preserved by a Laplacian graph regularization term on the low-dimensional data space. The projection matrix can serve as an importance indicator of different genes. An iterative update algorithm is developed for solving the problem. Experimental results on six publicly available microarray datasets and one clinical dataset demonstrate that the proposed method performs better when compared with other state-of-the-art methods in terms of microarray data classification. Graphical Abstract The graphical abstract of this work.

The paper analyzes the early theory building process of Thomas Hunt Morgan (1866-1945) from the 1910s to the 1930s and the introduction of the invisible gene as a main explanatory unit of heredity. Morgan's work marks the transition between two different styles of thought. In the early 1900s, he shifted from an embryological study of the development of the organism to a study of the mechanism of genetic inheritance and gene action. According to his contemporaries as well as to historiography, Morgan separated genetics from embryology, and the gene from the whole organism. Other scholars identified an underlying embryological focus in Morgan's work throughout his career. Our paper aims to clarify the debate by concentrating on Morgan's theory building-characterized by his confidence in the power of experimental methods, and carefully avoiding any ontological commitment towards the gene-and on the continuity of the questions to be addressed by both embryology and genetics.

The drug-gene interaction database (DGIdb, www.dgidb.org) consolidates, organizes and presents drug-gene interactions and gene druggability information from papers, databases and web resources. DGIdb normalizes content from 30 disparate sources and allows for user-friendly advanced browsing, searching and filtering for ease of access through an intuitive web user interface, application programming interface (API) and public cloud-based server image. DGIdb v3.0 represents a major update of the database. Nine of the previously included 24 sources were updated. Six new resources were added, bringing the total number of sources to 30. These updates and additions of sources have cumulatively resulted in 56 309 interaction claims. This has also substantially expanded the comprehensive catalogue of druggable genes and anti-neoplastic drug-gene interactions included in the DGIdb. Along with these content updates, v3.0 has received a major overhaul of its codebase, including an updated user interface, preset interaction search filters, consolidation of interaction information into interaction groups, greatly improved search response times and upgrading the underlying web application framework. In addition, the expanded API features new endpoints which allow users to extract more detailed information about queried drugs, genes and drug-gene interactions, including listings of PubMed IDs, interaction type and other interaction metadata.

Selecting informative genes, including individually discriminant genes and synergic genes, from expression data has been useful for medical diagnosis and prognosis. Detecting synergic genes is more difficult than selecting individually discriminant genes. Several efforts have recently been made to detect gene-gene synergies, such as dendrogram-based I(X 1 ; X 2 ; Y) (mutual information), doublets (gene pairs) and MIC(X 1 ; X 2 ; Y) based on the maximal information coefficient. It is unclear whether dendrogram-based I(X 1 ; X 2 ; Y) and doublets can capture synergies efficiently. Although MIC(X 1 ; X 2 ; Y) can capture a wide range of interaction, it has a high computational cost triggered by its 3-D search. In this paper, we developed a simple and fast approach based on abs conversion type (i.e. Z = |X 1  - X 2 |) and t-test, to detect interactions in simulation and real-world datasets. Our results showed that dendrogram-based I(X 1 ; X 2 ; Y) and doublets are helpless for discovering pair-wise gene interactions, our approach can discover typical pair-wise synergic genes efficiently. These synergic genes can reach comparable accuracy to the individually discriminant genes using the same number of genes. Classifier cannot learn well if synergic genes have not been converted properly. Combining individually discriminant and synergic genes can improve the prediction performance.

Background: Classification of biological samples of gene expression data is a basic building block in solving several problems in the field of bioinformatics like cancer and other disease diagnosis and making a proper treatment plan. One big challenge in sample classification is handling large dimensional and redundant gene expression data. To reduce the complexity of handling this high dimensional data, gene/feature selection plays a major role., Results: The current paper explores the use of biological knowledge acquired from Gene Ontology database in selecting the proper subset of genes which can further participate in clustering of samples. The proposed feature selection technique is unsupervised in nature as it does not utilize any class label information in the process of gene selection. At the end, a multi-objective clustering approach is deployed to cluster the available set of samples in the reduced gene space., Conclusions: Reported results show that consideration of biological knowledge in gene selection technique not only reduces the feature space dimensionality in great extent but also improves the accuracy of sample classification. The obtained reduced gene space is validated using strong biological significance tests. In order to prove the supremacy of our proposed gene selection based sample clustering technique, a thorough comparative analysis has also been performed with state-of-the-art techniques.

Background: The recent release of the gene-targeted metagenomics assembler Xander has demonstrated that using the trained Hidden Markov Model (HMM) to guide the traversal of de Bruijn graph gives obvious advantage over other assembly methods. Xander, as a pilot study, indeed has a lot of room for improvement. Apart from its slow speed, Xander uses only 1 k-mer size for graph construction and whatever choice of k will compromise either sensitivity or accuracy. Xander uses a Bloom-filter representation of de Bruijn graph to achieve a lower memory footprint. Bloom filters bring in false positives, and it is not clear how this would impact the quality of assembly. Xander does not keep track of the multiplicity of k-mers, which would have been an effective way to differentiate between erroneous k-mers and correct k-mers., Results: In this paper, we present a new gene-targeted assembler MegaGTA, which attempts to improve Xander in different aspects. Quality-wise, it utilizes iterative de Bruijn graphs to take full advantage of multiple k-mer sizes to make the best of both sensitivity and accuracy. Computation-wise, it employs succinct de Bruijn graphs (SdBG) to achieve low memory footprint and high speed (the latter is benefited from a highly efficient parallel algorithm for constructing SdBG). Unlike Bloom filters, an SdBG is an exact representation of a de Bruijn graph. It enables MegaGTA to avoid false-positive contigs and to easily incorporate the multiplicity of k-mers for building better HMM model. We have compared MegaGTA and Xander on an HMP-defined mock metagenomic dataset, and showed that MegaGTA excelled in both sensitivity and accuracy. On a large rhizosphere soil metagenomic sample (327Gbp), MegaGTA produced 9.7-19.3% more contigs than Xander, and these contigs were assigned to 10-25% more gene references. In our experiments, MegaGTA, depending on the number of k-mers used, is two to ten times faster than Xander., Conclusion: MegaGTA improves on the algorithm of Xander and achieves higher sensitivity, accuracy and speed. Moreover, it is capable of assembling gene sequences from ultra-large metagenomic datasets. Its source code is freely available at https://github.com/HKU-BAL/megagta .

Background: Microsoft Excel automatically converts certain gene symbols, database accessions, and other alphanumeric text into dates, scientific notation, and other numerical representations. These conversions lead to subsequent, irreversible, corruption of the imported text. A recent survey of popular genomic literature estimates that one-fifth of all papers with supplementary gene lists suffer from this issue., Results: Here, we present an open-source tool, Escape Excel, which prevents these erroneous conversions by generating an escaped text file that can be safely imported into Excel. Escape Excel is implemented in a variety of formats (http://www.github.com/pstew/escape&#95;excel), including a command line based Perl script, a Windows-only Excel Add-In, an OS X drag-and-drop application, a simple web-server, and as a Galaxy web environment interface. Test server implementations are accessible as a Galaxy interface (http://apostl.moffitt.org) and simple non-Galaxy web server (http://apostl.moffitt.org:8000/)., Conclusions: Escape Excel detects and escapes a wide variety of problematic text strings so that they are not erroneously converted into other representations upon importation into Excel. Examples of problematic strings include date-like strings, time-like strings, leading zeroes in front of numbers, and long numeric and alphanumeric identifiers that should not be automatically converted into scientific notation. It is hoped that greater awareness of these potential data corruption issues, together with diligent escaping of text files prior to importation into Excel, will help to reduce the amount of Excel-corrupted data in scientific analyses and publications.