scholarly journals Improved representation of sequence Bloom trees

2019 ◽  
Author(s):  
Robert S Harris ◽  
Paul Medvedev
Keyword(s):  
Supplementary Information ◽  
Supplementary File ◽  
Biological Databases ◽  
Rna Seq ◽  
End User ◽  
Indexing Methods ◽  
Source Program ◽  
Supplementary Text ◽  
Free Open Source ◽  
Generation Sequencing

Abstract Motivation Algorithmic solutions to index and search biological databases are a fundamental part of bioinformatics, providing underlying components to many end-user tools. Inexpensive next generation sequencing has filled publicly available databases such as the Sequence Read Archive beyond the capacity of traditional indexing methods. Recently, the Sequence Bloom Tree (SBT) and its derivatives were proposed as a way to efficiently index such data for queries about transcript presence. Results We build on the SBT framework to construct the HowDe-SBT data structure, which uses a novel partitioning of information to reduce the construction and query time as well as the size of the index. Compared to previous SBT methods on real RNA-seq data, HowDe-SBT can construct the index in less than 36% of the time, and with 39% less space, and can answer small-batch queries at least five times faster. We also develop a theoretical framework in which we can analyze and bound the space and query performance of HowDe-SBT compared to other SBT methods. Availability and implementation HowDe-SBT is available as a free open source program on https://github.com/medvedevgroup/HowDeSBT. Supplementary information Supplementary text and figures available as single Supplementary file.

scholarly journals Improved representation of sequence Bloom trees

2018 ◽  
Author(s):  
Robert S. Harris ◽  
Paul Medvedev
Keyword(s):  
Data Structure ◽  
Biological Databases ◽  
Rna Seq ◽  
End User ◽  
Indexing Methods ◽  
Fundamental Part ◽  
Sequence Read Archive ◽  
Source Program ◽  
Free Open Source ◽  
Generation Sequencing

AbstractAlgorithmic solutions to index and search biological databases are a fundamental part of bioinformatics, providing underlying components to many end-user tools. Inexpensive next generation sequencing has filled publicly available databases such as the Sequence Read Archive beyond the capacity of traditional indexing methods. Recently, the Sequence Bloom Tree (SBT) and its derivatives were proposed as a way to efficiently index such data for queries about transcript presence. We build on the SBT framework to construct the HowDe-SBT data structure, which uses a novel partitioning of information to reduce the construction and query time as well as the size of the index. We evaluate HowDe-SBT by both proving theoretical bounds on its performance and using real RNA-seq data. Compared to previous SBT methods, HowDe-SBT can construct the index in less than 36% the time, and with 39% less space, and can answer small-batch queries at least five times faster. HowDe-SBT is available as a free open source program on https://github.com/medvedevgroup/HowDeSBT.

scholarly journals BioSeqZip: a collapser of NGS redundant reads for the optimization of sequence analysis

2020 ◽  
Vol 36 (9) ◽  
pp. 2705-2711 ◽  
Author(s):  
Gianvito Urgese ◽  
Emanuele Parisi ◽  
Orazio Scicolone ◽  
Santa Di Cataldo ◽  
Elisa Ficarra
Keyword(s):  
Sequence Analysis ◽  
Supplementary Information ◽  
Sorting Algorithm ◽  
Rna Seq ◽  
Compact Sets ◽  
Analysis Pipeline ◽  
Alignment Algorithms ◽  
External Sorting ◽  
Computational Resources ◽  
Generation Sequencing

Abstract Motivation High-throughput next-generation sequencing can generate huge sequence files, whose analysis requires alignment algorithms that are typically very demanding in terms of memory and computational resources. This is a significant issue, especially for machines with limited hardware capabilities. As the redundancy of the sequences typically increases with coverage, collapsing such files into compact sets of non-redundant reads has the 2-fold advantage of reducing file size and speeding-up the alignment, avoiding to map the same sequence multiple times. Method BioSeqZip generates compact and sorted lists of alignment-ready non-redundant sequences, keeping track of their occurrences in the raw files as well as of their quality score information. By exploiting a memory-constrained external sorting algorithm, it can be executed on either single- or multi-sample datasets even on computers with medium computational capabilities. On request, it can even re-expand the compacted files to their original state. Results Our extensive experiments on RNA-Seq data show that BioSeqZip considerably brings down the computational costs of a standard sequence analysis pipeline, with particular benefits for the alignment procedures that typically have the highest requirements in terms of memory and execution time. In our tests, BioSeqZip was able to compact 2.7 billion of reads into 963 million of unique tags reducing the size of sequence files up to 70% and speeding-up the alignment by 50% at least. Availability and implementation BioSeqZip is available at https://github.com/bioinformatics-polito/BioSeqZip. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals CoCo: RNA-seq read assignment correction for nested genes and multimapped reads

2019 ◽  
Vol 35 (23) ◽  
pp. 5039-5047 ◽  
Author(s):  
Gabrielle Deschamps-Francoeur ◽  
Vincent Boivin ◽  
Sherif Abou Elela ◽  
Michelle S Scott
Keyword(s):  
Supplementary Information ◽  
Rna Seq ◽  
Non Coding Rna ◽  
Abundance Estimates ◽  
Gene Coverage ◽  
Nested Genes ◽  
Quantification Accuracy ◽  
Whole Transcriptome Analysis ◽  
Whole Transcriptome ◽  
Generation Sequencing

Abstract Motivation Next-generation sequencing techniques revolutionized the study of RNA expression by permitting whole transcriptome analysis. However, sequencing reads generated from nested and multi-copy genes are often either misassigned or discarded, which greatly reduces both quantification accuracy and gene coverage. Results Here we present count corrector (CoCo), a read assignment pipeline that takes into account the multitude of overlapping and repetitive genes in the transcriptome of higher eukaryotes. CoCo uses a modified annotation file that highlights nested genes and proportionally distributes multimapped reads between repeated sequences. CoCo salvages over 15% of discarded aligned RNA-seq reads and significantly changes the abundance estimates for both coding and non-coding RNA as validated by PCR and bedgraph comparisons. Availability and implementation The CoCo software is an open source package written in Python and available from http://gitlabscottgroup.med.usherbrooke.ca/scott-group/coco. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Holistic optimization of an RNA-seq workflow for multi-threaded environments

2019 ◽  
Vol 35 (20) ◽  
pp. 4173-4175 ◽  
Author(s):  
Ling-Hong Hung ◽  
Wes Lloyd ◽  
Radhika Agumbe Sridhar ◽  
Saranya Devi Athmalingam Ravishankar ◽  
Yuguang Xiong ◽  
...  
Keyword(s):  
Parallel Implementation ◽  
Reference Sequence ◽  
Supplementary Information ◽  
Rna Seq ◽  
Computationally Intensive ◽  
Holistic Optimization ◽  
Parallel Workflow ◽  
Unique Molecular Identifier ◽  
Generation Sequencing ◽  
Alignment Step

Abstract Summary For many next generation-sequencing pipelines, the most computationally intensive step is the alignment of reads to a reference sequence. As a result, alignment software such as the Burrows-Wheeler Aligner is optimized for speed and is often executed in parallel on the cloud. However, there are other less demanding steps that can also be optimized to significantly increase the speed especially when using many threads. We demonstrate this using a unique molecular identifier RNA-sequencing pipeline consisting of 3 steps: split, align, and merge. Optimization of all three steps yields a 40% increase in speed when executed using a single thread. However, when executed using 16 threads, we observe a 4-fold improvement over the original parallel implementation and more than an 8-fold improvement over the original single-threaded implementation. In contrast, optimizing only the alignment step results in just a 13% improvement over the original parallel workflow using 16 threads. Availability and implementation Code (M.I.T. license), supporting scripts and Dockerfiles are available at https://github.com/BioDepot/LINCS_RNAseq_cpp and Docker images at https://hub.docker.com/r/biodepot/rnaseq-umi-cpp/ Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals Easymap: A User-Friendly Software Package for Rapid Mapping-by-Sequencing of Point Mutations and Large Insertions

2021 ◽  
Vol 12 ◽  
Author(s):  
Samuel Daniel Lup ◽  
David Wilson-Sánchez ◽  
Sergio Andreu-Sánchez ◽  
José Luis Micol
Keyword(s):  
Point Mutations ◽  
Rapid Identification ◽  
Graphical Interface ◽  
Rna Seq ◽  
Source Program ◽  
Next Generation Sequencing Ngs ◽  
User Friendly ◽  
Ngs Data ◽  
Mapping By Sequencing ◽  
Generation Sequencing

Mapping-by-sequencing strategies combine next-generation sequencing (NGS) with classical linkage analysis, allowing rapid identification of the causal mutations of the phenotypes exhibited by mutants isolated in a genetic screen. Computer programs that analyze NGS data obtained from a mapping population of individuals derived from a mutant of interest to identify a causal mutation are available; however, the installation and usage of such programs requires bioinformatic skills, modifying or combining pieces of existing software, or purchasing licenses. To ease this process, we developed Easymap, an open-source program that simplifies the data analysis workflows from raw NGS reads to candidate mutations. Easymap can perform bulked segregant mapping of point mutations induced by ethyl methanesulfonate (EMS) with DNA-seq or RNA-seq datasets, as well as tagged-sequence mapping for large insertions, such as transposons or T-DNAs. The mapping analyses implemented in Easymap have been validated with experimental and simulated datasets from different plant and animal model species. Easymap was designed to be accessible to all users regardless of their bioinformatics skills by implementing a user-friendly graphical interface, a simple universal installation script, and detailed mapping reports, including informative images and complementary data for assessment of the mapping results. Easymap is available at http://genetics.edu.umh.es/resources/easymap; its Quickstart Installation Guide details the recommended procedure for installation.

scholarly journals SnakeChunks: modular blocks to build Snakemake workflows for reproducible NGS analyses

2017 ◽  
Author(s):  
Claire Rioualen ◽  
Lucie Charbonnier-Khamvongsa ◽  
Jacques van Helden
Keyword(s):  
Next Generation Sequencing ◽  
Life Sciences ◽  
Supplementary Information ◽  
Supplementary Data ◽  
Rna Seq ◽  
Genome Wide ◽  
Domains Of Life ◽  
Supplementary Material ◽  
Next Generation Sequencing Ngs ◽  
Generation Sequencing

AbstractSummaryNext-Generation Sequencing (NGS) is becoming a routine approach for most domains of life sciences, yet there is a crucial need to improve the automation of processing for the huge amounts of data generated and to ensure reproducible results. We present SnakeChunks, a collection of Snakemake rules enabling to compose modular and user-configurable workflows, and show its usage with analyses of transcriptome (RNA-seq) and genome-wide location (ChIP-seq) data.AvailabilityThe code is freely available (github.com/SnakeChunks/SnakeChunks), and documented with tutorials and illustrative demos (snakechunks.readthedocs.io)[email protected], [email protected] informationSupplementary data are available at Bioinformatics online.

scholarly journals pyBedGraph: a python package for fast operations on 1D genomic signal tracks

2020 ◽  
Vol 36 (10) ◽  
pp. 3234-3235
Author(s):  
Henry B Zhang ◽  
Minji Kim ◽  
Jeffrey H Chuang ◽  
Yijun Ruan
Keyword(s):  
Chromatin Accessibility ◽  
Genomic Research ◽  
Supplementary Information ◽  
Summary Statistics ◽  
Rna Seq ◽  
Binary Format ◽  
Modern Genomic ◽  
Binding Intensity ◽  
Python Package ◽  
Generation Sequencing

Abstract Motivation Modern genomic research is driven by next-generation sequencing experiments such as ChIP-seq and ChIA-PET that generate coverage files for transcription factor binding, as well as DHS and ATAC-seq that yield coverage files for chromatin accessibility. Such files are in a bedGraph text format or a bigWig binary format. Obtaining summary statistics in a given region is a fundamental task in analyzing protein binding intensity or chromatin accessibility. However, the existing Python package for operating on coverage files is not optimized for speed. Results We developed pyBedGraph, a Python package to quickly obtain summary statistics for a given interval in a bedGraph or a bigWig file. When tested on 12 ChIP-seq, ATAC-seq, RNA-seq and ChIA-PET datasets, pyBedGraph is on average 260 times faster than the existing program pyBigWig. On average, pyBedGraph can look up the exact mean signal of 1 million regions in ∼0.26 s and can compute their approximate means in <0.12 s on a conventional laptop. Availability and implementation pyBedGraph is publicly available at https://github.com/TheJacksonLaboratory/pyBedGraph under the MIT license. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals BAMixChecker: an automated checkup tool for matched sample pairs in NGS cohort

2019 ◽  
Vol 35 (22) ◽  
pp. 4806-4808 ◽  
Author(s):  
Hein Chun ◽  
Sangwoo Kim
Keyword(s):  
Genomic Analysis ◽  
Supplementary Information ◽  
Nucleotide Polymorphisms ◽  
Rna Seq ◽  
Sequencing Data ◽  
Single Nucleotide ◽  
Frequent Problem ◽  
Generation Sequencing ◽  
User Intervention ◽  
Genotype Concordance

Abstract Summary Mislabeling in the process of next generation sequencing is a frequent problem that can cause an entire genomic analysis to fail, and a regular cohort-level checkup is needed to ensure that it has not occurred. We developed a new, automated tool (BAMixChecker) that accurately detects sample mismatches from a given BAM file cohort with minimal user intervention. BAMixChecker uses a flexible, data-specific set of single-nucleotide polymorphisms and detects orphan (unpaired) and swapped (mispaired) samples based on genotype-concordance score and entropy-based file name analysis. BAMixChecker shows ∼100% accuracy in real WES, RNA-Seq and targeted sequencing data cohorts, even for small panels (<50 genes). BAMixChecker provides an HTML-style report that graphically outlines the sample matching status in tables and heatmaps, with which users can quickly inspect any mismatch events. Availability and implementation BAMixChecker is available at https://github.com/heinc1010/BAMixChecker Supplementary information Supplementary data are available at Bioinformatics online.

scRNAss: a single-cell RNA-seq assembler via imputing dropouts and combing junctions

2019 ◽  
Vol 35 (21) ◽  
pp. 4264-4271
Author(s):  
Juntao Liu ◽  
Xiangyu Liu ◽  
Xianwen Ren ◽  
Guojun Li
Keyword(s):  
Single Cell ◽  
Open Source Software ◽  
State Of The Art ◽  
Supplementary Information ◽  
Rna Seq ◽  
Transcript Reconstruction ◽  
Full Length Transcript ◽  
Free Open Source ◽  
Transcript Assembly ◽  
Novel Isoforms

Abstract Motivation Full-length transcript reconstruction is essential for single-cell RNA-seq data analysis, but dropout events, which can cause transcripts discarded completely or broken into pieces, pose great challenges for transcript assembly. Currently available RNA-seq assemblers are generally designed for bulk RNA sequencing. To fill the gap, we introduce single-cell RNA-seq assembler, a method that applies explicit strategies to impute lost information caused by dropout events and a combing strategy to infer transcripts using scRNA-seq. Results Extensive evaluations on both simulated and biological datasets demonstrated its superiority over the state-of-the-art RNA-seq assemblers including StringTie, Cufflinks and CLASS2. In particular, it showed a remarkable capability of recovering unknown ‘novel’ isoforms and highly computational efficiency compared to other tools. Availability and implementation scRNAss is free, open-source software available from https://sourceforge.net/projects/single-cell-rna-seq-assembly/files/. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals REINDEER: efficient indexing of k-mer presence and abundance in sequencing datasets

2020 ◽  
Vol 36 (Supplement_1) ◽  
pp. i177-i185
Author(s):  
Camille Marchet ◽  
Zamin Iqbal ◽  
Daniel Gautheret ◽  
Mikaël Salson ◽  
Rayan Chikhi
Keyword(s):  
Large Datasets ◽  
Computational Method ◽  
Supplementary Information ◽  
De Bruijn Graph ◽  
Supplementary Data ◽  
Rna Seq ◽  
Indexing Methods ◽  
De Bruijn Graphs ◽  
De Bruijn

Abstract Motivation In this work we present REINDEER, a novel computational method that performs indexing of sequences and records their abundances across a collection of datasets. To the best of our knowledge, other indexing methods have so far been unable to record abundances efficiently across large datasets. Results We used REINDEER to index the abundances of sequences within 2585 human RNA-seq experiments in 45 h using only 56 GB of RAM. This makes REINDEER the first method able to record abundances at the scale of ∼4 billion distinct k-mers across 2585 datasets. REINDEER also supports exact presence/absence queries of k-mers. Briefly, REINDEER constructs the compacted de Bruijn graph of each dataset, then conceptually merges those de Bruijn graphs into a single global one. Then, REINDEER constructs and indexes monotigs, which in a nutshell are groups of k-mers of similar abundances. Availability and implementation https://github.com/kamimrcht/REINDEER. Supplementary information Supplementary data are available at Bioinformatics online.

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