ngsLD: evaluating linkage disequilibrium using genotype likelihoods

Abstract Motivation Linkage disequilibrium (LD) measures the correlation between genetic loci and is highly informative for association mapping and population genetics. As many studies rely on called genotypes for estimating LD, their results can be affected by data uncertainty, especially when employing a low read depth sequencing strategy. Furthermore, there is a manifest lack of tools for the analysis of large-scale, low-depth and short-read sequencing data from non-model organisms with limited sample sizes. Results ngsLD addresses these issues by estimating LD directly from genotype likelihoods in a fast, reliable and user-friendly implementation. This method makes use of the full information available from sequencing data and provides accurate estimates of linkage disequilibrium patterns compared with approaches based on genotype calling. We conducted a case study to investigate how LD decays over physical distance in two avian species. Availability and implementation The methods presented in this work were implemented in C/C and are freely available for non-commercial use from https://github.com/fgvieira/ngsLD. Supplementary information Supplementary data are available at Bioinformatics online.

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Global sequence characterization of rice centromeric satellite based on oligomer frequency analysis in large-scale sequencing data

Bioinformatics ◽

10.1093/bioinformatics/btq343 ◽

2010 ◽

Vol 26 (17) ◽

pp. 2101-2108 ◽

Cited By ~ 27

Author(s):

Jiří Macas ◽

Pavel Neumann ◽

Petr Novák ◽

Jiming Jiang

Keyword(s):

Large Scale ◽

Rice Genome ◽

Supplementary Information ◽

Sequencing Data ◽

Satellite Repeat ◽

Frequency Spectra ◽

Consensus Sequences ◽

Chip Sequencing ◽

Conserved Sequence ◽

Centromeric Satellite

Abstract Motivation: Satellite DNA makes up significant portion of many eukaryotic genomes, yet it is relatively poorly characterized even in extensively sequenced species. This is, in part, due to methodological limitations of traditional methods of satellite repeat analysis, which are based on multiple alignments of monomer sequences. Therefore, we employed an alternative, alignment-free, approach utilizing k-mer frequency statistics, which is in principle more suitable for analyzing large sets of satellite repeat data, including sequence reads from next generation sequencing technologies. Results: k-mer frequency spectra were determined for two sets of rice centromeric satellite CentO sequences, including 454 reads from ChIP-sequencing of CENH3-bound DNA (7.6 Mb) and the whole genome Sanger sequencing reads (5.8 Mb). k-mer frequencies were used to identify the most conserved sequence regions and to reconstruct consensus sequences of complete monomers. Reconstructed consensus sequences as well as the assessment of overall divergence of k-mer spectra revealed high similarity of the two datasets, suggesting that CentO sequences associated with functional centromeres (CENH3-bound) do not significantly differ from the total population of CentO, which includes both centromeric and pericentromeric repeat arrays. On the other hand, considerable differences were revealed when these methods were used for comparison of CentO populations between individual chromosomes of the rice genome assembly, demonstrating preferential sequence homogenization of the clusters within the same chromosome. k-mer frequencies were also successfully used to identify and characterize smRNAs derived from CentO repeats. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics online.

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AStrap: identification of alternative splicing from transcript sequences without a reference genome

Bioinformatics ◽

10.1093/bioinformatics/bty1008 ◽

2018 ◽

Vol 35 (15) ◽

pp. 2654-2656 ◽

Cited By ~ 5

Author(s):

Guoli Ji ◽

Wenbin Ye ◽

Yaru Su ◽

Moliang Chen ◽

Guangzao Huang ◽

...

Keyword(s):

Machine Learning ◽

Alternative Splicing ◽

Single Molecule ◽

Reference Genome ◽

De Novo ◽

Supplementary Information ◽

Model Organisms ◽

Sequencing Data ◽

Extensive Evaluation ◽

Reference Genomes

Abstract Summary Alternative splicing (AS) is a well-established mechanism for increasing transcriptome and proteome diversity, however, detecting AS events and distinguishing among AS types in organisms without available reference genomes remains challenging. We developed a de novo approach called AStrap for AS analysis without using a reference genome. AStrap identifies AS events by extensive pair-wise alignments of transcript sequences and predicts AS types by a machine-learning model integrating more than 500 assembled features. We evaluated AStrap using collected AS events from reference genomes of rice and human as well as single-molecule real-time sequencing data from Amborella trichopoda. Results show that AStrap can identify much more AS events with comparable or higher accuracy than the competing method. AStrap also possesses a unique feature of predicting AS types, which achieves an overall accuracy of ∼0.87 for different species. Extensive evaluation of AStrap using different parameters, sample sizes and machine-learning models on different species also demonstrates the robustness and flexibility of AStrap. AStrap could be a valuable addition to the community for the study of AS in non-model organisms with limited genetic resources. Availability and implementation AStrap is available for download at https://github.com/BMILAB/AStrap. Supplementary information Supplementary data are available at Bioinformatics online.

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A Universal, Genomewide GuideFinder for CRISPR/Cas9 Targeting in Microbial Genomes

mSphere ◽

10.1128/msphere.00086-20 ◽

2020 ◽

Vol 5 (1) ◽

Author(s):

Michelle Spoto ◽

Changhui Guan ◽

Elizabeth Fleming ◽

Julia Oh

Keyword(s):

Gene Function ◽

Large Scale ◽

Essential Gene ◽

Bacterial Species ◽

Bacterial Genome ◽

Model Organisms ◽

Design Parameters ◽

Bacterial Genomes ◽

Wide Range ◽

User Friendly

ABSTRACT The CRISPR/Cas system has significant potential to facilitate gene editing in a variety of bacterial species. CRISPR interference (CRISPRi) and CRISPR activation (CRISPRa) represent modifications of the CRISPR/Cas9 system utilizing a catalytically inactive Cas9 protein for transcription repression and activation, respectively. While CRISPRi and CRISPRa have tremendous potential to systematically investigate gene function in bacteria, few programs are specifically tailored to identify guides in draft bacterial genomes genomewide. Furthermore, few programs offer open-source code with flexible design parameters for bacterial targeting. To address these limitations, we created GuideFinder, a customizable, user-friendly program that can design guides for any annotated bacterial genome. GuideFinder designs guides from NGG protospacer-adjacent motif (PAM) sites for any number of genes by the use of an annotated genome and FASTA file input by the user. Guides are filtered according to user-defined design parameters and removed if they contain any off-target matches. Iteration with lowered parameter thresholds allows the program to design guides for genes that did not produce guides with the more stringent parameters, one of several features unique to GuideFinder. GuideFinder can also identify paired guides for targeting multiplicity, whose validity we tested experimentally. GuideFinder has been tested on a variety of diverse bacterial genomes, finding guides for 95% of genes on average. Moreover, guides designed by the program are functionally useful—focusing on CRISPRi as a potential application—as demonstrated by essential gene knockdown in two staphylococcal species. Through the large-scale generation of guides, this open-access software will improve accessibility to CRISPR/Cas studies of a variety of bacterial species. IMPORTANCE With the explosion in our understanding of human and environmental microbial diversity, corresponding efforts to understand gene function in these organisms are strongly needed. CRISPR/Cas9 technology has revolutionized interrogation of gene function in a wide variety of model organisms. Efficient CRISPR guide design is required for systematic gene targeting. However, existing tools are not adapted for the broad needs of microbial targeting, which include extraordinary species and subspecies genetic diversity, the overwhelming majority of which is characterized by draft genomes. In addition, flexibility in guide design parameters is important to consider the wide range of factors that can affect guide efficacy, many of which can be species and strain specific. We designed GuideFinder, a customizable, user-friendly program that addresses the limitations of existing software and that can design guides for any annotated bacterial genome with numerous features that facilitate guide design in a wide variety of microorganisms.

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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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gplas: a comprehensive tool for plasmid analysis using short-read graphs

Bioinformatics ◽

10.1093/bioinformatics/btaa233 ◽

2020 ◽

Vol 36 (12) ◽

pp. 3874-3876 ◽

Cited By ~ 1

Author(s):

Sergio Arredondo-Alonso ◽

Martin Bootsma ◽

Yaïr Hein ◽

Malbert R C Rogers ◽

Jukka Corander ◽

...

Keyword(s):

Large Scale ◽

Sequence Data ◽

Bacterial Genome ◽

Workflow Management ◽

Supplementary Information ◽

Whole Genome Sequencing Data ◽

Network Partitioning ◽

Sequencing Data ◽

Genetic Traits ◽

Short Read

Abstract Summary Plasmids can horizontally transmit genetic traits, enabling rapid bacterial adaptation to new environments and hosts. Short-read whole-genome sequencing data are often applied to large-scale bacterial comparative genomics projects but the reconstruction of plasmids from these data is facing severe limitations, such as the inability to distinguish plasmids from each other in a bacterial genome. We developed gplas, a new approach to reliably separate plasmid contigs into discrete components using sequence composition, coverage, assembly graph information and network partitioning based on a pruned network of plasmid unitigs. Gplas facilitates the analysis of large numbers of bacterial isolates and allows a detailed analysis of plasmid epidemiology based solely on short-read sequence data. Availability and implementation Gplas is written in R, Bash and uses a Snakemake pipeline as a workflow management system. Gplas is available under the GNU General Public License v3.0 at https://gitlab.com/sirarredondo/gplas.git. Supplementary information Supplementary data are available at Bioinformatics online.

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Quantification of aneuploidy in targeted sequencing data using ASCETS

Bioinformatics ◽

10.1093/bioinformatics/btaa980 ◽

2020 ◽

Author(s):

Liam F Spurr ◽

Mehdi Touat ◽

Alison M Taylor ◽

Adrian M Dubuc ◽

Juliann Shih ◽

...

Keyword(s):

Copy Number ◽

Large Scale ◽

Genomic Analysis ◽

Targeted Sequencing ◽

Supplementary Information ◽

Supplementary Data ◽

Sequencing Data ◽

Copy Number Changes ◽

Panel Sequencing ◽

Chromosome Level

Abstract Summary The expansion of targeted panel sequencing efforts has created opportunities for large-scale genomic analysis, but tools for copy-number quantification on panel data are lacking. We introduce ASCETS, a method for the efficient quantitation of arm and chromosome-level copy-number changes from targeted sequencing data. Availability and implementation ASCETS is implemented in R and is freely available to non-commercial users on GitHub: https://github.com/beroukhim-lab/ascets, along with detailed documentation. Supplementary information Supplementary data are available at Bioinformatics online.

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SECAPR - A bioinformatics pipeline for the rapid and user-friendly alignment of hybrid enrichment sequences, from raw reads to alignments

10.7287/peerj.preprints.26477v2 ◽

2018 ◽

Author(s):

Tobias Andermann ◽

Angela Cano ◽

Alexander Zizka ◽

Christine Bacon ◽

Alexandre Antonelli

Keyword(s):

Evolutionary Biology ◽

Sequence Data ◽

Model Organisms ◽

Sequencing Data ◽

Bioinformatics Pipeline ◽

Sequence Alignments ◽

Multiple Sequence ◽

Sequence Capture ◽

Sequencing Platforms ◽

User Friendly

Evolutionary biology has entered an era of unprecedented amounts of DNA sequence data, as new sequencing platforms such as Massive Parallel Sequencing (MPS) can generate billions of nucleotides within less than a day. The current bottleneck is how to efficiently handle, process, and analyze such large amounts of data in an automated and reproducible way. To tackle these challenges we introduce the Sequence Capture Processor (SECAPR) pipeline for processing raw sequencing data into multiple sequence alignments for downstream phylogenetic and phylogeographic analyses. SECAPR is user-friendly and we provide an exhaustive tutorial intended for users with no prior experience with analyzing MPS output. SECAPR is particularly useful for the processing of sequence capture (= hybrid enrichment) datasets for non-model organisms, as we demonstrate using an empirical dataset of the palm genus Geonoma (Arecaceae). Various quality control and plotting functions help the user to decide on the most suitable settings for even challenging datasets. SECAPR is an easy-to-use, free, and versatile pipeline, aimed to enable efficient and reproducible processing of MPS data for many samples in parallel.

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Transcriptional Landscape and Splicing Efficiency in Arabidopsis Mitochondria

Cells ◽

10.3390/cells10082054 ◽

2021 ◽

Vol 10 (8) ◽

pp. 2054

Author(s):

Laura E. Garcia ◽

M. Virginia Sanchez-Puerta

Keyword(s):

Large Scale ◽

Read Depth ◽

Sequencing Data ◽

Sequence Comparisons ◽

Mitochondrial Transcription ◽

Multiple Promoters ◽

Transcription Process ◽

Splicing Efficiency ◽

Diverse Plant Species ◽

Transcriptional Landscape

Plant mitochondrial transcription is initiated from multiple promoters without an apparent motif, which precludes their identification in other species based on sequence comparisons. Even though coding regions take up only a small fraction of plant mitochondrial genomes, deep RNAseq studies uncovered that these genomes are fully or nearly fully transcribed with significantly different RNA read depth across the genome. Transcriptomic analysis can be a powerful tool to understand the transcription process in diverse angiosperms, including the identification of potential promoters and co-transcribed genes or to study the efficiency of intron splicing. In this work, we analyzed the transcriptional landscape of the Arabidopsis mitochondrial genome (mtDNA) based on large-scale RNA sequencing data to evaluate the use of RNAseq to study those aspects of the transcription process. We found that about 98% of the Arabidopsis mtDNA is transcribed with highly different RNA read depth, which was elevated in known genes. The location of a sharp increase in RNA read depth upstream of genes matched the experimentally identified promoters. The continuously high RNA read depth across two adjacent genes agreed with the known co-transcribed units in Arabidopsis mitochondria. Most intron-containing genes showed a high splicing efficiency with no differences between cis and trans-spliced introns or between genes with distinct splicing mechanisms. Deep RNAseq analyses of diverse plant species will be valuable to recognize general and lineage-specific characteristics related to the mitochondrial transcription process.

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DECA: scalable XHMM exome copy-number variant calling with ADAM and Apache Spark

BMC Bioinformatics ◽

10.1186/s12859-019-3108-7 ◽

2019 ◽

Vol 20 (1) ◽

Cited By ~ 2

Author(s):

Michael D. Linderman ◽

Davin Chia ◽

Forrest Wallace ◽

Frank A. Nothaft

Keyword(s):

Copy Number ◽

Large Scale ◽

Variant Calling ◽

Copy Number Variant ◽

Read Depth ◽

Lessons Learned ◽

Apache Spark ◽

Sequencing Data ◽

Whole Exome Sequencing Data ◽

Genome Analyses

Abstract Background XHMM is a widely used tool for copy-number variant (CNV) discovery from whole exome sequencing data but can require hours to days to run for large cohorts. A more scalable implementation would reduce the need for specialized computational resources and enable increased exploration of the configuration parameter space to obtain the best possible results. Results DECA is a horizontally scalable implementation of the XHMM algorithm using the ADAM framework and Apache Spark that incorporates novel algorithmic optimizations to eliminate unneeded computation. DECA parallelizes XHMM on both multi-core shared memory computers and large shared-nothing Spark clusters. We performed CNV discovery from the read-depth matrix in 2535 exomes in 9.3 min on a 16-core workstation (35.3× speedup vs. XHMM), 12.7 min using 10 executor cores on a Spark cluster (18.8× speedup vs. XHMM), and 9.8 min using 32 executor cores on Amazon AWS’ Elastic MapReduce. We performed CNV discovery from the original BAM files in 292 min using 640 executor cores on a Spark cluster. Conclusions We describe DECA’s performance, our algorithmic and implementation enhancements to XHMM to obtain that performance, and our lessons learned porting a complex genome analysis application to ADAM and Spark. ADAM and Apache Spark are a performant and productive platform for implementing large-scale genome analyses, but efficiently utilizing large clusters can require algorithmic optimizations and careful attention to Spark’s configuration parameters.

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Scirpy: a Scanpy extension for analyzing single-cell T-cell receptor-sequencing data

Bioinformatics ◽

10.1093/bioinformatics/btaa611 ◽

2020 ◽

Vol 36 (18) ◽

pp. 4817-4818 ◽

Cited By ~ 2

Author(s):

Gregor Sturm ◽

Tamas Szabo ◽

Georgios Fotakis ◽

Marlene Haider ◽

Dietmar Rieder ◽

...

Keyword(s):

T Cell ◽

Single Cell ◽

Large Scale ◽

Single Cells ◽

Cell Receptor ◽

Supplementary Information ◽

Sequencing Data ◽

Seamless Integration ◽

T Cell Phenotypes ◽

Cell Phenotypes

Abstract Summary Advances in single-cell technologies have enabled the investigation of T-cell phenotypes and repertoires at unprecedented resolution and scale. Bioinformatic methods for the efficient analysis of these large-scale datasets are instrumental for advancing our understanding of adaptive immune responses. However, while well-established solutions are accessible for the processing of single-cell transcriptomes, no streamlined pipelines are available for the comprehensive characterization of T-cell receptors. Here, we propose single-cell immune repertoires in Python (Scirpy), a scalable Python toolkit that provides simplified access to the analysis and visualization of immune repertoires from single cells and seamless integration with transcriptomic data. Availability and implementation Scirpy source code and documentation are available at https://github.com/icbi-lab/scirpy. Supplementary information Supplementary data are available at Bioinformatics online.

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