SomVarIUS: somatic variant identification from unpaired tissue samples

Abstract Motivation: Somatic variant calling typically requires paired tumor-normal tissue samples. Yet, paired normal tissues are not always available in clinical settings or for archival samples. Results: We present SomVarIUS, a computational method for detecting somatic variants using high throughput sequencing data from unpaired tissue samples. We evaluate the performance of the method using genomic data from synthetic and real tumor samples. SomVarIUS identifies somatic variants in exome-seq data of ∼150 × coverage with at least 67.7% precision and 64.6% recall rates, when compared with paired-tissue somatic variant calls in real tumor samples. We demonstrate the utility of SomVarIUS by identifying somatic mutations in formalin-fixed samples, and tracking clonal dynamics of oncogenic mutations in targeted deep sequencing data from pre- and post-treatment leukemia samples. Availability and implementation: SomVarIUS is written in Python 2.7 and available at http://www.sjdlab.org/resources/ Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

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deepBase v3.0: expression atlas and interactive analysis of ncRNAs from thousands of deep-sequencing data

Nucleic Acids Research ◽

10.1093/nar/gkaa1039 ◽

2020 ◽

Vol 49 (D1) ◽

pp. D877-D883

Author(s):

Fangzhou Xie ◽

Shurong Liu ◽

Junhao Wang ◽

Jiajia Xuan ◽

Xiaoqin Zhang ◽

...

Keyword(s):

High Throughput Sequencing ◽

Clinical Information ◽

Sequencing Data ◽

Normal Tissues ◽

Interactive Analysis ◽

High Throughput Sequencing Data ◽

Expression Atlas ◽

Expression Evolution ◽

Noninvasive Biomarkers ◽

Cancer Tissues

Abstract Eukaryotic genomes encode thousands of small and large non-coding RNAs (ncRNAs). However, the expression, functions and evolution of these ncRNAs are still largely unknown. In this study, we have updated deepBase to version 3.0 (deepBase v3.0, http://rna.sysu.edu.cn/deepbase3/index.html), an increasingly popular and openly licensed resource that facilitates integrative and interactive display and analysis of the expression, evolution, and functions of various ncRNAs by deeply mining thousands of high-throughput sequencing data from tissue, tumor and exosome samples. We updated deepBase v3.0 to provide the most comprehensive expression atlas of small RNAs and lncRNAs by integrating ∼67 620 data from 80 normal tissues and ∼50 cancer tissues. The extracellular patterns of various ncRNAs were profiled to explore their applications for discovery of noninvasive biomarkers. Moreover, we constructed survival maps of tRNA-derived RNA Fragments (tRFs), miRNAs, snoRNAs and lncRNAs by analyzing >45 000 cancer sample data and corresponding clinical information. We also developed interactive webs to analyze the differential expression and biological functions of various ncRNAs in ∼50 types of cancers. This update is expected to provide a variety of new modules and graphic visualizations to facilitate analyses and explorations of the functions and mechanisms of various types of ncRNAs.

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circtools—a one-stop software solution for circular RNA research

Bioinformatics ◽

10.1093/bioinformatics/bty948 ◽

2018 ◽

Vol 35 (13) ◽

pp. 2326-2328 ◽

Cited By ~ 13

Author(s):

Tobias Jakobi ◽

Alexey Uvarovskii ◽

Christoph Dieterich

Keyword(s):

High Throughput Sequencing ◽

Circular Rna ◽

Statistical Testing ◽

Supplementary Information ◽

Circular Rnas ◽

Sequencing Data ◽

High Throughput Sequencing Data ◽

Multi Stage ◽

Sequence Reconstruction ◽

One Stop

Abstract Motivation Circular RNAs (circRNAs) originate through back-splicing events from linear primary transcripts, are resistant to exonucleases, are not polyadenylated and have been shown to be highly specific for cell type and developmental stage. CircRNA detection starts from high-throughput sequencing data and is a multi-stage bioinformatics process yielding sets of potential circRNA candidates that require further analyses. While a number of tools for the prediction process already exist, publicly available analysis tools for further characterization are rare. Our work provides researchers with a harmonized workflow that covers different stages of in silico circRNA analyses, from prediction to first functional insights. Results Here, we present circtools, a modular, Python-based framework for computational circRNA analyses. The software includes modules for circRNA detection, internal sequence reconstruction, quality checking, statistical testing, screening for enrichment of RBP binding sites, differential exon RNase R resistance and circRNA-specific primer design. circtools supports researchers with visualization options and data export into commonly used formats. Availability and implementation circtools is available via https://github.com/dieterich-lab/circtools and http://circ.tools under GPLv3.0. Supplementary information Supplementary data are available at Bioinformatics online.

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hypeR: An R Package for Geneset Enrichment Workflows

10.1101/656637 ◽

2019 ◽

Cited By ~ 1

Author(s):

Anthony Federico ◽

Stefano Monti

Keyword(s):

High Throughput Sequencing ◽

R Package ◽

Supplementary Information ◽

Sequencing Data ◽

Wide Audience ◽

Popular Method ◽

Link Type ◽

High Throughput Sequencing Data ◽

One Stop ◽

Recent Version

ABSTRACTSummaryGeneset enrichment is a popular method for annotating high-throughput sequencing data. Existing tools fall short in providing the flexibility to tackle the varied challenges researchers face in such analyses, particularly when analyzing many signatures across multiple experiments. We present a comprehensive R package for geneset enrichment workflows that offers multiple enrichment, visualization, and sharing methods in addition to novel features such as hierarchical geneset analysis and built-in markdown reporting. hypeR is a one-stop solution to performing geneset enrichment for a wide audience and range of use cases.Availability and implementationThe most recent version of the package is available at https://github.com/montilab/hypeR.Supplementary informationComprehensive documentation and tutorials, are available at https://montilab.github.io/hypeR-docs.

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ADFinder: accurate detection of programmed DNA elimination using NGS high-throughput sequencing data

Bioinformatics ◽

10.1093/bioinformatics/btaa226 ◽

2020 ◽

Vol 36 (12) ◽

pp. 3632-3636 ◽

Cited By ~ 2

Author(s):

Weibo Zheng ◽

Jing Chen ◽

Thomas G Doak ◽

Weibo Song ◽

Ying Yan

Keyword(s):

High Throughput ◽

Large Scale ◽

High Throughput Sequencing ◽

Supplementary Information ◽

Sequencing Data ◽

Source Codes ◽

High Throughput Sequencing Data ◽

Dna Elimination ◽

Multiple Alternative ◽

Dna Splicing

Abstract Motivation Programmed DNA elimination (PDE) plays a crucial role in the transitions between germline and somatic genomes in diverse organisms ranging from unicellular ciliates to multicellular nematodes. However, software specific for the detection of DNA splicing events is scarce. In this paper, we describe Accurate Deletion Finder (ADFinder), an efficient detector of PDEs using high-throughput sequencing data. ADFinder can predict PDEs with relatively low sequencing coverage, detect multiple alternative splicing forms in the same genomic location and calculate the frequency for each splicing event. This software will facilitate research of PDEs and all down-stream analyses. Results By analyzing genome-wide DNA splicing events in two micronuclear genomes of Oxytricha trifallax and Tetrahymena thermophila, we prove that ADFinder is effective in predicting large scale PDEs. Availability and implementation The source codes and manual of ADFinder are available in our GitHub website: https://github.com/weibozheng/ADFinder. Supplementary information Supplementary data are available at Bioinformatics online.

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NGSEP3: accurate variant calling across species and sequencing protocols

Bioinformatics ◽

10.1093/bioinformatics/btz275 ◽

2019 ◽

Vol 35 (22) ◽

pp. 4716-4723 ◽

Cited By ~ 7

Author(s):

Daniel Tello ◽

Juanita Gil ◽

Cristian D Loaiza ◽

John J Riascos ◽

Nicolás Cardozo ◽

...

Keyword(s):

Short Tandem Repeats ◽

Tandem Repeats ◽

High Throughput Sequencing ◽

Variant Calling ◽

Real Data ◽

Supplementary Information ◽

Sequencing Data ◽

Comparative Accuracy ◽

Downstream Analysis ◽

Short Tandem

Abstract Motivation Accurate detection, genotyping and downstream analysis of genomic variants from high-throughput sequencing data are fundamental features in modern production pipelines for genetic-based diagnosis in medicine or genomic selection in plant and animal breeding. Our research group maintains the Next-Generation Sequencing Experience Platform (NGSEP) as a precise, efficient and easy-to-use software solution for these features. Results Understanding that incorrect alignments around short tandem repeats are an important source of genotyping errors, we implemented in NGSEP new algorithms for realignment and haplotype clustering of reads spanning indels and short tandem repeats. We performed extensive benchmark experiments comparing NGSEP to state-of-the-art software using real data from three sequencing protocols and four species with different distributions of repetitive elements. NGSEP consistently shows comparative accuracy and better efficiency compared to the existing solutions. We expect that this work will contribute to the continuous improvement of quality in variant calling needed for modern applications in medicine and agriculture. Availability and implementation NGSEP is available as open source software at http://ngsep.sf.net. Supplementary information Supplementary data are available at Bioinformatics online.

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Inference of viral quasispecies with a paired de Bruijn graph

Bioinformatics ◽

10.1093/bioinformatics/btaa782 ◽

2020 ◽

Author(s):

Borja Freire ◽

Susana Ladra ◽

Jose R Paramá ◽

Leena Salmela

Keyword(s):

High Throughput Sequencing ◽

De Novo ◽

Supplementary Information ◽

De Bruijn Graph ◽

Viral Quasispecies ◽

Sequencing Data ◽

De Bruijn Graphs ◽

Sequencing Errors ◽

High Throughput Sequencing Data ◽

De Bruijn

Abstract Motivation RNA viruses exhibit a high mutation rate and thus they exist in infected cells as a population of closely related strains called viral quasispecies. The viral quasispecies assembly problem asks to characterize the quasispecies present in a sample from high-throughput sequencing data. We study the de novo version of the problem, where reference sequences of the quasispecies are not available. Current methods for assembling viral quasispecies are either based on overlap graphs or on de Bruijn graphs. Overlap graph-based methods tend to be accurate but slow, whereas de Bruijn graph-based methods are fast but less accurate. Results We present viaDBG, which is a fast and accurate de Bruijn graph-based tool for de novo assembly of viral quasispecies. We first iteratively correct sequencing errors in the reads, which allows us to use large k-mers in the de Bruijn graph. To incorporate the paired-end information in the graph, we also adapt the paired de Bruijn graph for viral quasispecies assembly. These features enable the use of long-range information in contig construction without compromising the speed of de Bruijn graph-based approaches. Our experimental results show that viaDBG is both accurate and fast, whereas previous methods are either fast or accurate but not both. In particular, viaDBG has comparable or better accuracy than SAVAGE, while being at least nine times faster. Furthermore, the speed of viaDBG is comparable to PEHaplo but viaDBG is able to retrieve also low abundance quasispecies, which are often missed by PEHaplo. Availability and implementation viaDBG is implemented in C++ and it is publicly available at https://bitbucket.org/bfreirec1/viadbg. All datasets used in this article are publicly available at https://bitbucket.org/bfreirec1/data-viadbg/. Supplementary information Supplementary data are available at Bioinformatics online.

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SomatoSim: precision simulation of somatic single nucleotide variants

BMC Bioinformatics ◽

10.1186/s12859-021-04024-8 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Marwan A. Hawari ◽

Celine S. Hong ◽

Leslie G. Biesecker

Keyword(s):

High Throughput Sequencing ◽

Variant Calling ◽

Simulated Data ◽

Sequencing Data ◽

Single Nucleotide Variants ◽

Single Nucleotide ◽

Somatic Variant ◽

Simulation Tools ◽

Gold Standard Dataset ◽

High Level

Abstract Background Somatic single nucleotide variants have gained increased attention because of their role in cancer development and the widespread use of high-throughput sequencing techniques. The necessity to accurately identify these variants in sequencing data has led to a proliferation of somatic variant calling tools. Additionally, the use of simulated data to assess the performance of these tools has become common practice, as there is no gold standard dataset for benchmarking performance. However, many existing somatic variant simulation tools are limited because they rely on generating entirely synthetic reads derived from a reference genome or because they do not allow for the precise customizability that would enable a more focused understanding of single nucleotide variant calling performance. Results SomatoSim is a tool that lets users simulate somatic single nucleotide variants in sequence alignment map (SAM/BAM) files with full control of the specific variant positions, number of variants, variant allele fractions, depth of coverage, read quality, and base quality, among other parameters. SomatoSim accomplishes this through a three-stage process: variant selection, where candidate positions are selected for simulation, variant simulation, where reads are selected and mutated, and variant evaluation, where SomatoSim summarizes the simulation results. Conclusions SomatoSim is a user-friendly tool that offers a high level of customizability for simulating somatic single nucleotide variants. SomatoSim is available at https://github.com/BieseckerLab/SomatoSim.

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MitoFlex: an efficient, high-performance toolkit for animal mitogenome assembly, annotation, and visualization

Bioinformatics ◽

10.1093/bioinformatics/btab111 ◽

2021 ◽

Author(s):

Jun-Yu Li ◽

Wei-Xuan Li ◽

An-Tai Wang ◽

Zhang Yu

Keyword(s):

Mitochondrial Genome ◽

High Performance ◽

High Throughput Sequencing ◽

De Novo ◽

Supplementary Information ◽

Sequencing Data ◽

Protein Coding ◽

High Throughput Sequencing Data ◽

Genome Analysis Toolkit ◽

Overall Performance

Abstract Summary MitoFlex is a linux-based mitochondrial genome analysis toolkit, which provides a complete workflow of raw data filtering, de novo assembly, mitochondrial genome identification and annotation for animal high throughput sequencing data. The overall performance was compared between MitoFlex and its analogue MitoZ, in terms of protein coding gene recovery, memory consumption and processing speed. Availability MitoFlex is available at https://github.com/Prunoideae/MitoFlex under GPLv3 license. Supplementary information Supplementary data are available at Bioinformatics online.

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Measuring reproducibility of virus metagenomics analyses using bootstrap samples from FASTQ-files

Bioinformatics ◽

10.1093/bioinformatics/btaa926 ◽

2020 ◽

Author(s):

Babak Saremi ◽

Moritz Kohls ◽

Pamela Liebig ◽

Ursula Siebert ◽

Klaus Jung

Keyword(s):

Mixture Model ◽

High Throughput Sequencing ◽

Supplementary Information ◽

Sequencing Data ◽

Bootstrap Sampling ◽

Technical Errors ◽

High Throughput Sequencing Data ◽

The Difference ◽

Real World Datasets ◽

Highly Correlated

Abstract Motivation High-throughput sequencing data can be affected by different technical errors, e.g. from probe preparation or false base calling. As a consequence, reproducibility of experiments can be weakened. In virus metagenomics, technical errors can result in falsely identified viruses in samples from infected hosts. We present a new resampling approach based on bootstrap sampling of sequencing reads from FASTQ-files in order to generate artificial replicates of sequencing runs which can help to judge the robustness of an analysis. In addition, we evaluate a mixture model on the distribution of read counts per virus to identify potentially false positive findings. Results The evaluation of our approach on an artificially generated dataset with known viral sequence content shows in general a high reproducibility of uncovering viruses in sequencing data, i.e. the correlation between original and mean bootstrap read count was highly correlated. However, the bootstrap read counts can also indicate reduced or increased evidence for the presence of a virus in the biological sample. We also found that the mixture-model fits well to the read counts, and furthermore, it provides a higher accuracy on the original or on the bootstrap read counts than on the difference between both. The usefulness of our methods is further demonstrated on two freely available real-world datasets from harbor seals. Availability and implementation We provide a Phyton tool, called RESEQ, available from https://github.com/babaksaremi/RESEQ that allows efficient generation of bootstrap reads from an original FASTQ-file. Supplementary information Supplementary data are available at Bioinformatics online.

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