Search

Motivation Calling copy number alterations (CNAs) from RNA sequencing (RNA-Seq) is challenging, because of the marked variability in coverage across genes and paucity of single nucleotide polymorphisms (SNPs). We have adapted SuperFreq to call absolute and allele sensitive CNAs from RNA-Seq. SuperFreq uses an error-propagation framework to combine and maximize information from read counts and B-allele frequencies. Results We used datasets from The Cancer Genome Atlas (TCGA) to assess the validity of CNA calls from RNA-Seq. When ploidy estimates were consistent, we found agreement with DNA SNP-arrays for over 98% of the genome for acute myeloid leukaemia (TCGA-AML, n  = 116) and 87% for colorectal cancer (TCGA-CRC, n  = 377). The sensitivity of CNA calling from RNA-Seq was dependent on gene density. Using RNA-Seq, SuperFreq detected 78% of CNA calls covering 100 or more genes with a precision of 94%. Recall dropped for focal events, but this also depended on signal intensity. For example, in the CRC cohort SuperFreq identified all cases (7/7) with high-level amplification of ERBB2 , where the copy number was typically >20, but identified only 6% of cases (1/17) with moderate amplification of IGF2 , which occurs over a smaller interval. SuperFreq offers an integrated platform for identification of CNAs and point mutations. As evidence of how SuperFreq can be applied, we used it to reproduce the established relationship between somatic mutation load and CNA profile in CRC using RNA-Seq alone. Availability and implementation SuperFreq is implemented in R and the code is available through GitHub: https://github.com/ChristofferFlensburg/SuperFreq/. Data and code to reproduce the figures are available at: https://gitlab.wehi.edu.au/flensburg.c/SuperFreq%5fRNA%5fpaper. Data from TCGA (phs000178) was accessed from GDC following completion of a data access request through the database of Genotypes and Phenotypes (dbGaP). Data from the Leucegene consortium was downloaded from GEO (AML samples: GSE67040; normal CD34+ cells: GSE48846). Supplementary information Supplementary data are available at Bioinformatics online. [ABSTRACT FROM AUTHOR]

As single-cell RNA-sequencing (scRNA-seq) datasets have become more widespread the number of tools designed to analyse these data has dramatically increased. Navigating the vast sea of tools now available is becoming increasingly challenging for researchers. In order to better facilitate selection of appropriate analysis tools we have created the scRNA-tools database () to catalogue and curate analysis tools as they become available. Our database collects a range of information on each scRNA-seq analysis tool and categorises them according to the analysis tasks they perform. Exploration of this database gives insights into the areas of rapid development of analysis methods for scRNA-seq data. We see that many tools perform tasks specific to scRNA-seq analysis, particularly clustering and ordering of cells. We also find that the scRNA-seq community embraces an open-source and open-science approach, with most tools available under open-source licenses and preprints being extensively used as a means to describe methods. The scRNA-tools database provides a valuable resource for researchers embarking on scRNA-seq analysis and records the growth of the field over time. [ABSTRACT FROM AUTHOR]

Background: The forelimb of the flightless emu is a vestigial structure, with greatly reduced wing elements and digit loss. To explore the molecular and cellular mechanisms associated with the evolution of vestigial wings and loss of flight in the emu, key limb patterning genes were examined in developing embryos. Methods: Limb development was compared in emu versus chicken embryos. Immunostaining for cell proliferation markers was used to analyze growth of the emu forelimb and hindlimb buds. Expression patterns of limb patterning genes were studied, using whole-mount in situ hybridization (for mRNA localization) and RNA-seq (for mRNA expression levels). Results: The forelimb of the emu embryo showed heterochronic development compared to that in the chicken, with the forelimb bud being retarded in its development. Early outgrowth of the emu forelimb bud is characterized by a lower level of cell proliferation compared the hindlimb bud, as assessed by PH3 immunostaining. In contrast, there were no obvious differences in apoptosis in forelimb versus hindlimb buds (cleaved caspase 3 staining). Most key patterning genes were expressed in emu forelimb buds similarly to that observed in the chicken, but with smaller expression domains. However, expression of Sonic Hedgehog (Shh) mRNA, which is central to anterior'posterior axis development, was delayed in the emu forelimb bud relative to other patterning genes. Regulators of Shh expression, Gli3 and HoxD13, also showed altered expression levels in the emu forelimb bud. Conclusions: These data reveal heterochronic but otherwise normal expression of most patterning genes in the emu vestigial forelimb. Delayed Shh expression may be related to the small and vestigial structure of the emu forelimb bud. However, the genetic mechanism driving retarded emu wing development is likely to rest within the forelimb field of the lateral plate mesoderm, predating the expression of patterning genes. [ABSTRACT FROM AUTHOR]

Background: Despite some advances in recent years, the genetic control of gonadal sex differentiation during embryogenesis is still not completely understood. To identify new candidate genes involved in ovary and testis development, RNA-seq was used to define the transcriptome of embryonic chicken gonads at the onset of sexual differentiation (day 6.0/stage 29). Results: RNA-seq revealed more than 1000 genes that were transcribed in a sex-biased manner at this early stage of gonadal differentiation. Comparison with undifferentiated gonads revealed that sex biased expression was derived primarily from autosomal rather than sex-linked genes. Gene ontology and pathway analysis indicated that many of these genes encoded proteins involved in extracellular matrix function and cytoskeletal remodelling, as well as tubulogenesis. Several of these genes are novel candidate regulators of gonadal sex differentiation, based on sex-biased expression profiles that are altered following experimental sex reversal. We further characterised three female-biased (ovarian) genes; calpain-5 (CAPN5), G-protein coupled receptor 56 (GPR56), and FGFR3 (fibroblast growth factor receptor 3). Protein expression of these candidates in the developing ovaries suggests that they play an important role in this tissue. Conclusions: This study provides insight into the earliest steps of vertebrate gonad sex differentiation, and identifies novel candidate genes for ovarian and testicular development. [ABSTRACT FROM AUTHOR]

Genomic instability is a hallmark of cancer and, as such, structural alterations and fusion genes are common events in the cancer landscape. RNA sequencing (RNA-Seq) is a powerful method for profiling cancers, but current methods for identifying fusion genes are optimised for short reads. JAFFA (https://github.com/Oshlack/JAFFA/wiki) is a sensitive fusion detection method that outperforms other methods with reads of 100 bp or greater. JAFFA compares a cancer transcriptome to the reference transcriptome, rather than the genome, where the cancer transcriptome is inferred using long reads directly or by de novo assembling short reads. [ABSTRACT FROM AUTHOR]

Background: Congenital cytomegalovirus infection is the most common perinatal infection and a significant cause of sensorineural hearing loss, cerebral palsy, and neurodevelopmental disability. There is a paucity of human gene expression studies examining the pathophysiology of cytomegalovirus infection.Objective: This study aimed to perform a whole transcriptomic assessment of amniotic fluid from pregnancies with live fetuses to identify differentially expressed genes and enriched Gene Ontology categories associated with congenital cytomegalovirus infection.Study Design: Amniotic fluid supernatant was prospectively collected from pregnant women undergoing amniocentesis for suspected congenital cytomegalovirus infection because of first-trimester maternal primary infection or ultrasound features suggestive of fetal infection. Women who had received therapy to prevent fetal infection were excluded. Congenital cytomegalovirus infection was diagnosed via viral polymerase chain reaction of amniotic fluid; cytomegalovirus-infected fetuses were paired with noninfected controls, matched for gestational age and fetal sex. Paired-end RNA sequencing was performed on amniotic fluid cell-free RNA with the Novaseq 6000 at a depth of 30 million reads per sample. Following quality control and filtering, reads were mapped to the human genome and counts summarized across genes. Differentially expressed genes were identified using 2 approaches: voomWithQualityWeights in conjunction with limma and RUVSeq with edgeR. Genes with a false discovery rate <0.05 were considered statistically significant. Differential exon use was analyzed using DEXSeq. Functional analysis was performed using gene set enrichment analysis and Ingenuity Pathway Analysis. Manual curation of differentially regulated genes was also performed.Results: Amniotic fluid samples were collected from 50 women; 16 (32%) had congenital cytomegalovirus infection confirmed by polymerase chain reaction. After excluding 3 samples without matched controls, 13 cytomegalovirus-infected samples collected at 18 to 23 weeks and 13 cytomegalovirus-negative gestation-matched controls were submitted for RNA sequencing and analysis (N=26). Ten of the 13 pregnancies with cytomegalovirus-infected fetuses had amniocentesis because of serologic evidence of maternal primary infection with normal fetal ultrasound, and 3 had amniocentesis because of ultrasound abnormality suggestive of cytomegalovirus infection. Four cytomegalovirus-infected pregnancies ended in termination (n=3) or fetal death (n=1), and 9 resulted in live births. Pregnancy outcomes were available for 11 of the 13 cytomegalovirus-negative controls; all resulted in live births of clinically-well infants. Differential gene expression analysis revealed 309 up-regulated and 32 down-regulated genes in the cytomegalovirus-infected group compared with the cytomegalovirus-negative group. Gene set enrichment analysis showed significant enrichment of multiple Gene Ontology categories involving the innate immune response to viral infection and interferon signaling. Of the 32 significantly down-regulated genes, 8 were known to be involved in neurodevelopment and preferentially expressed by the brain. Six specific cellular restriction factors involved in host defense to cytomegalovirus infection were up-regulated in the cytomegalovirus-infected group. Ingenuity Pathway Analysis predicted the activation of pathways involved in progressive neurologic disease and inflammatory neurologic disease.Conclusion: In this next-generation sequencing study, we revealed new insights into the pathophysiology of congenital cytomegalovirus infection. These data on the up-regulation of the intraamniotic innate immune response to cytomegalovirus infection and the dysregulation of neurodevelopmental genes may inform future approaches to developing prognostic markers and assessing fetal responses to in utero therapy. [ABSTRACT FROM AUTHOR]

Clustering techniques are widely used in the analysis of large datasets to group together samples with similar properties. For example, clustering is often used in the field of single-cell RNA-sequencing in order to identify different cell types present in a tissue sample. There are many algorithms for performing clustering, and the results can vary substantially. In particular, the number of groups present in a dataset is often unknown, and the number of clusters identified by an algorithm can change based on the parameters used. To explore and examine the impact of varying clustering resolution, we present clustering trees. This visualization shows the relationships between clusters at multiple resolutions, allowing researchers to see how samples move as the number of clusters increases. In addition, meta-information can be overlaid on the tree to inform the choice of resolution and guide in identification of clusters. We illustrate the features of clustering trees using a series of simulations as well as two real examples, the classical iris dataset and a complex single-cell RNA-sequencing dataset. Clustering trees can be produced using the clustree R package, available from CRAN and developed on GitHub. [ABSTRACT FROM AUTHOR]

Background RNA sequencing (RNA-seq) analyses can benefit from performing a genome-guided and de novo assembly, in particular for species where the reference genome or the annotation is incomplete. However, tools for integrating an assembled transcriptome with reference annotation are lacking. Findings Necklace is a software pipeline that runs genome-guided and de novo assembly and combines the resulting transcriptomes with reference genome annotations. Necklace constructs a compact but comprehensive superTranscriptome out of the assembled and reference data. Reads are subsequently aligned and counted in preparation for differential expression testing. Conclusions Necklace allows a comprehensive transcriptome to be built from a combination of assembled and annotated transcripts, which results in a more comprehensive transcriptome for the majority of organisms. In addition RNA-seq data are mapped back to this newly created superTranscript reference to enable differential expression testing with standard methods. [ABSTRACT FROM AUTHOR]