Biology subjects

Wilks, C.

Publications and source records attributed to Wilks, C..

9 recordsLinked to original sources

bioRxiv2022

Unleashing alternative polyadenylation analyses with REPAC

Alternative polyadenylation (APA) is an important post-transcriptional mechanism that has major implications in biological processes and diseases. Although specialized sequencing methods for polyadenylation exist, their presence in public repositories is extremely limited when compared to traditional RNA-sequencing. To overcome this, we developed REPAC, a framework for the analysis of APA from RNA-sequencing data. REPAC implements a new method for detection of APA and is designed to take advantage of recount3 which enables a streamlined way to analyze over 750,000 publicly available samples. Using REPAC, we investigated the landscape of APA caused by activation of B cells. Our analysis revealed that during this process, hundreds of genes are regulated by APA, most notably genes involved in the secretion pathway which is central for the transition to antibody-secreting B-cells. Moreover, we also showed that many genes associated with interferon response are also shortened, suggesting that APA might also play a significant role in the immune response. We also show that REPAC is faster than alternative methods by at least 7-fold and that it scales well to analysis involving hundreds of samples. Overall, the REPAC method offers an accurate, easy, and convenient solution for the exploration of APA across many phenotypes.

bioinformatics↗

bioRxiv2021

recount3: summaries and queries for large-scale RNA-seq expression and splicing

We present recount3, a resource consisting of over 750,000 publicly available human and mouse RNA sequencing (RNA-seq) samples uniformly processed by our new Monorail analysis pipeline. To facilitate access to the data, we provide the recount3 and snapcount R/Bioconductor packages as well as complementary web resources. Using these tools, data can be downloaded as study-level summaries or queried for specific exon-exon junctions, genes, samples, or other features. Monorail can be used to process local and/or private data, allowing results to be directly compared to any study in recount3. Taken together, our tools help biologists maximize the utility of publicly available RNA-seq data, especially to improve their understanding of newly collected data. recount3 is available from http://rna.recount.bio.

genomics↗

bioRxiv2020

Megadepth: efficient coverage quantification for BigWigs and BAMs

MotivationA common way to summarize sequencing datasets is to quantify data lying within genes or other genomic intervals. This can be slow and can require different tools for different input file types. ResultsMegadepth is a fast tool for quantifying alignments and coverage for BigWig and BAM/CRAM input files, using substantially less memory than the next-fastest competitor. Megadepth can summarize coverage within all disjoint intervals of the Gencode V35 gene annotation for more than 19,000 GTExV8 BigWig files in approximately one hour using 32 threads. Megadepth is available both as a command-line tool and as an R/Bioconductor package providing much faster quantification compared to the rtracklayer package. Availabilityhttps://github.com/ChristopherWilks/megadepth, https://bioconductor.org/packages/megadepth. Contactchris.wilks@jhu.edu

bioinformatics↗

bioRxiv2020

LongTron: Automated Analysis of Long Read Spliced Alignment Accuracy

MotivationLong read sequencing has increased the accuracy and completeness of assemblies of various organisms genomes in recent months. Similarly, spliced alignments of long read RNA sequencing hold the promise of delivering much longer transcripts of existing and novel isoforms in known genes without the need for error-prone transcript assemblies from short reads. However, low coverage and high-error rates potentially hamper the widespread adoption of long-read spliced alignments in annotation updates and isoform-level expression quantifications. ResultsAddressing these issues, we first develop a simulation of error modes for both Oxford Nanopore and PacBio CCS spliced-alignments. Based on this we train a Random Forest classifier to assign new long-read alignments to one of two error categories, a novel category, or label them as non-error. We use this classifier to label reads from the spliced-alignments of the popular aligner minimap2, run on three long read sequencing datasets, including NA12878 from Oxford Nanopore and PacBio CCS, as well as a PacBio SKBR3 cancer cell line. Finally, we compare the intron chains of the three long read alignments against individual splice sites, short read assemblies, and the output from the FLAIR pipeline on the same samples. Our results demonstrate a substantial lack of precision in determining exact splice sites for long reads during alignment on both platforms while showing some benefit from postprocessing. This work motivates the need for both better aligners and additional post-alignment processing to adjust incorrectly called putative splice-sites and clarify novel transcripts support. Availability and implementationSource code for the random forest implemented in python is available at https://github.com/schatzlab/LongTron under the MIT license. The modified version of GffCompare used to construct Table 3 and related is here: https://github.com/ChristopherWilks/gffcompare/releases/tag/0.11.2LT O_TBL View this table: org.highwire.dtl.DTLVardef@117255borg.highwire.dtl.DTLVardef@95a815org.highwire.dtl.DTLVardef@14e21faorg.highwire.dtl.DTLVardef@98e040org.highwire.dtl.DTLVardef@aab93e_HPS_FORMAT_FIGEXP M_TBL O_FLOATNOTable 3.C_FLOATNO O_TABLECAPTIONIsoform comparison table, using gene models from Gencode V29, plus the isoforms from all the union of annotations; both exact and fuzz comparisons of the set of long-read derived isoforms which 1) match in number of introns or 2) are contained or contain a reference isoform. Percentages use the "Total Intron Chains" as the denominator for the row. C_TABLECAPTION C_TBL Supplementary InformationSupplementary notes and figures are available online.

bioinformatics↗

bioRxiv2019

Recounting the FANTOM Cage Associated Transcriptome

Long non-coding RNAs (lncRNAs) have emerged as key coordinators of biological and cellular processes. Characterizing lncRNA expression across cells and tissues is key to understanding their role in determining phenotypes including human diseases. We present here FC-R2, a comprehensive expression atlas across a broadly-defined human transcriptome, inclusive of over 109,000 coding and non-coding genes, as described in the FANTOM CAGE-Associated Transcriptome (FANTOM-CAT) study. This atlas greatly extends the gene annotation used in the original recount2 resource. We demonstrate the utility of the FC-R2 atlas by reproducing key findings from published large studies and by generating new results across normal and diseased human samples. In particular, we (a) identify tissue specific transcription profiles for distinct classes of coding and non-coding genes, (b) perform differential expression analyses across thirteen cancer types, providing new insights linking promoter and enhancer lncRNAs expression to tumor pathogenesis, and (c) confirm the prognostic value of several enhancers in cancer. Comprised of over 70,000 samples, the FC-R2 atlas will empower other researchers to investigate functions and biological roles of both known coding genes and novel lncRNAs. Most importantly, access to the FC-R2 atlas is available from https://jhubiostatistics.shinyapps.io/recount/, the recount Bioconductor package, and http://marchionnilab.org/fcr2.html.

genomics↗

bioRxiv2019

Spatial distribution of Salmonella enterica in poultry shed environments observed by intensive longitudinal environmental sampling

During a series of epidemiological studies to investigate the transmission of salmonellae within poultry environments, intensive longitudinal sampling within caged sheds revealed that both the number and location of sample collection within this environment were important to ensure the best chance of detecting Salmonella spp. Multiple serovars of Salmonella enterica subspecies enterica were detected in each shed; 5% of all samples contained more than one serovar. Samples collected on the north side of the shed (OR = 1.77, 95% CI [1.17, 2.68]), on the sheltered side of the shed (OR = 1.90, 95% CI [1.26, 2.89]) and during winter (OR = 48.41, 95%CI [23.56, 104.19]) were more likely to be positive for salmonellae. The distribution of salmonellae within a shed was not homogenous, as differences were identified in the within-shed distribution of Salmonella enterica subspecies enterica serovar Typhimurium ({chi}2 (27, 1,538) = 54.4, P < 0.001), and Salmonella enterica subspecies enterica serovar Infantis ({chi}2 (27, 1,538) = 79.8, P < 0.0001). The difference in sample prevalence by serovar and spatial location within a shed indicates that there are important shed micro-environmental factors that influence survival and/or distribution of salmonellae. These factors should be taken into consideration when undertaking environmental surveillance for salmonellae in flocks housed in caged sheds. IMPORTANCERoutine epidemiological surveillance for salmonellae in poultry relies initially on environmental sampling. Our study confirmed that the sampling methodology conducted within a poultry environment is a non-trivial part of sampling design. The spatial locations of sample collection within a shed revealed differences in both the sample prevalence and serovar of salmonellae detected.

epidemiology↗

bioRxiv2018

ASCOT identifies key regulators of neuronal subtype-specific splicing

Public archives of next-generation sequencing data are growing exponentially, but the difficulty of marshaling this data has led to its underutilization by scientists. Here we present ASCOT, a resource that allows researchers to summarize, visualize, and query alternative splicing patterns in public RNA-Seq data. ASCOT enables rapid identification of splice-variants across tens of thousands of bulk and single-cell RNA-Seq datasets in human and mouse. To demonstrate the utility of ASCOT, we first focused on the nervous system and identified many alternative exons used only by a single neuronal subtype. We then leveraged datasets from the ENCODE and GTEx consortiums to study the unique splicing patterns of rod photoreceptors and found that PTBP1 knockdown combined with overexpression of MSI1 and PCBP2 activates rod-specific exons in HepG2 liver cancer cells. Furthermore, we observed that MSI1 targets intronic UAG motifs proximal to the 5 splice site and interacts synergistically with PTBP1 downregulation. Finally, we show that knockdown of MSI1 in the retina abolishes rod-specific splicing. This work demonstrates how large-scale analysis of public RNA-Seq datasets can yield key insights into cell type-specific control of RNA splicing and underscores the importance of considering both annotated and unannotated splicing events. ASCOT splicing and gene expression data tables, software, and interactive browser are available at http://ascot.cs.jhu.edu.

neuroscience↗

bioRxiv2017

Scaling read aligners to hundreds of threads on general-purpose processors

General-purpose processors can now contain many dozens of processor cores and support hundreds of simultaneous threads of execution. To make best use of these threads, genomics software must contend with new and subtle computer architecture issues. We discuss some of these and propose methods for improving thread scaling in tools that analyze each read independently, such as read aligners. We implement these methods in new versions of Bowtie, Bowtie 2 and HISAT. We greatly improve thread scaling in many scenarios, including on the recent Intel Xeon Phi architecture. We also highlight how bottlenecks are exacerbated by variable-record-length file formats like FASTQ and suggest changes that enable superior scaling.

bioinformatics↗

bioRxiv2017

Snaptron: querying and visualizing splicing across tens of thousands of RNA-seq samples

As more and larger genomics studies appear, there is a growing need for comprehensive and queryable cross-study summaries. Snaptron is a search engine for summarized RNA sequencing data with a query planner that leverages R-tree, B-tree and inverted indexing strategies to rapidly execute queries over 146 million exon-exon splice junctions from over 70,000 human RNA-seq samples. Queries can be tailored by constraining which junctions and samples to consider. Snaptron can also rank and score junctions according to tissue specificity or other criteria. Further, Snaptron can rank and score samples according to the relative frequency of different splicing patterns. We outline biological questions that can be explored with Snaptron queries, including a study of novel exons in annotated genes, of exonization of repetitive element loci, and of a recently discovered alternative transcription start site for the ALK gene. Web app and documentation are at http://snaptron.cs.jhu.edu. Source code is at https://github.com/ChristopherWilks/snaptron under the MIT license.

bioinformatics↗