scholarly journals FastqPuri: high-performance preprocessing of RNA-seq data

2018 ◽  
Author(s):  
Paula Pérez-Rubio ◽  
Claudio Lottaz ◽  
Julia C Engelmann
Keyword(s):  
High Performance ◽  
Sequence Data ◽  
Transcript Expression ◽  
Memory Usage ◽  
Rna Seq ◽  
Sequencing Data ◽  
Total Size ◽  
Short Read ◽  
Quality Filtering ◽  
Sequence Quality

AbstractBackgroundRNA sequencing (RNA-seq) has become the standard means of analyzing gene and transcript expression in high-throughput. While previously sequence alignment was a time demanding step, fast alignment methods and even more so transcript counting methods which avoid mapping and quantify gene and transcript expression by evaluating whether a read is compatible with a transcript, have led to significant speed-ups in data analysis. Now, the most time demanding step in the analysis of RNA-seq data is preprocessing the raw sequence data, such as running quality control and adapter, contamination and quality filtering before transcript or gene quantification. To do so, many researchers chain different tools, but a comprehensive, flexible and fast software that covers all preprocessing steps is currently missing.ResultsWe here present FastqPuri, a light-weight and highly efficient preprocessing tool for fastq data. FastqPuri provides sequence quality reports on the sample and dataset level with new plots which facilitate decision making for subsequent quality filtering. Moreover, FastqPuri efficiently removes adapter sequences and sequences from biological contamination from the data. It accepts both single- and paired-end data in uncompressed or compressed fastq files. FastqPuri can be run stand-alone and is suitable to be run within pipelines. We benchmarked FastqPuri against existing tools and found that FastqPuri is superior in terms of speed, memory usage, versatility and comprehensiveness. Conclusions: FastqPuri is a new tool which covers all aspects of short read sequence data preprocessing. It was designed for RNA-seq data to meet the needs for fast preprocessing of fastq data to allow transcript and gene counting, but it is suitable to process any short read sequencing data of which high sequence quality is needed, such as for genome assembly or SNV (single nucleotide variant) detection. FastqPuri is most flexible in filtering undesired biological sequences by offering two approaches to optimize speed and memory usage dependent on the total size of the potential contaminating sequences. FastqPuri is available at https://github.com/jengelmann/FastqPuri. It is implemented in C and R and licensed under GPL v3.

scholarly journals REscan: inferring repeat expansions and structural variation in paired-end short read sequencing data

2020 ◽  
Author(s):  
Russell Lewis McLaughlin
Keyword(s):  
Structural Variation ◽  
Sequence Data ◽  
Neurological Diseases ◽  
Repeat Expansion ◽  
Sequencing Data ◽  
Short Read ◽  
Short Read Sequencing ◽  
Repeat Expansions ◽  
Paired End Sequencing

Abstract Motivation Repeat expansions are an important class of genetic variation in neurological diseases. However, the identification of novel repeat expansions using conventional sequencing methods is a challenge due to their typical lengths relative to short sequence reads and difficulty in producing accurate and unique alignments for repetitive sequence. However, this latter property can be harnessed in paired-end sequencing data to infer the possible locations of repeat expansions and other structural variation. Results This article presents REscan, a command-line utility that infers repeat expansion loci from paired-end short read sequencing data by reporting the proportion of reads orientated towards a locus that do not have an adequately mapped mate. A high REscan statistic relative to a population of data suggests a repeat expansion locus for experimental follow-up. This approach is validated using genome sequence data for 259 cases of amyotrophic lateral sclerosis, of which 24 are positive for a large repeat expansion in C9orf72, showing that REscan statistics readily discriminate repeat expansion carriers from non-carriers. Availabilityand implementation C source code at https://github.com/rlmcl/rescan (GNU General Public Licence v3).

scholarly journals The long and the short of it: unlocking nanopore long-read RNA sequencing data with short-read differential expression analysis tools

2021 ◽  
Vol 3 (2) ◽  
Author(s):  
Xueyi Dong ◽  
Luyi Tian ◽  
Quentin Gouil ◽  
Hasaru Kariyawasam ◽  
Shian Su ◽  
...  
Keyword(s):  
Differential Expression ◽  
Expression Analysis ◽  
Differential Expression Analysis ◽  
Transcriptomic Analysis ◽  
Statistical Testing ◽  
Rna Seq ◽  
Sequencing Data ◽  
Short Read ◽  
Sequencing Platform ◽  
Long Read

Abstract Application of Oxford Nanopore Technologies’ long-read sequencing platform to transcriptomic analysis is increasing in popularity. However, such analysis can be challenging due to the high sequence error and small library sizes, which decreases quantification accuracy and reduces power for statistical testing. Here, we report the analysis of two nanopore RNA-seq datasets with the goal of obtaining gene- and isoform-level differential expression information. A dataset of synthetic, spliced, spike-in RNAs (‘sequins’) as well as a mouse neural stem cell dataset from samples with a null mutation of the epigenetic regulator Smchd1 was analysed using a mix of long-read specific tools for preprocessing together with established short-read RNA-seq methods for downstream analysis. We used limma-voom to perform differential gene expression analysis, and the novel FLAMES pipeline to perform isoform identification and quantification, followed by DRIMSeq and limma-diffSplice (with stageR) to perform differential transcript usage analysis. We compared results from the sequins dataset to the ground truth, and results of the mouse dataset to a previous short-read study on equivalent samples. Overall, our work shows that transcriptomic analysis of long-read nanopore data using long-read specific preprocessing methods together with short-read differential expression methods and software that are already in wide use can yield meaningful results.

scholarly journals Rapid Mycobacterium tuberculosis spoligotyping from uncorrected long reads using Galru

2020 ◽  
Author(s):  
Andrew J. Page ◽  
Nabil-Fareed Alikhan ◽  
Michael Strinden ◽  
Thanh Le Viet ◽  
Timofey Skvortsov
Keyword(s):  
Mycobacterium Tuberculosis ◽  
State Of The Art ◽  
Sequence Data ◽  
Human Pathogen ◽  
Sequencing Data ◽  
Short Read ◽  
Short Read Sequencing ◽  
Long Reads ◽  
Long Read

AbstractSpoligotyping of Mycobacterium tuberculosis provides a subspecies classification of this major human pathogen. Spoligotypes can be predicted from short read genome sequencing data; however, no methods exist for long read sequence data such as from Nanopore or PacBio. We present a novel software package Galru, which can rapidly detect the spoligotype of a Mycobacterium tuberculosis sample from as little as a single uncorrected long read. It allows for near real-time spoligotyping from long read data as it is being sequenced, giving rapid sample typing. We compare it to the existing state of the art software and find it performs identically to the results obtained from short read sequencing data. Galru is freely available from https://github.com/quadram-institute-bioscience/galru under the GPLv3 open source licence.

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

2020 ◽  
Vol 36 (12) ◽  
pp. 3874-3876 ◽  
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.

scholarly journals Fast and accurate HLA typing from short-read next-generation sequence data with xHLA

2017 ◽  
Vol 114 (30) ◽  
pp. 8059-8064 ◽  
Author(s):  
Chao Xie ◽  
Zhen Xuan Yeo ◽  
Marie Wong ◽  
Jason Piper ◽  
Tao Long ◽  
...  
Keyword(s):  
Sequence Data ◽  
Sequence Similarity ◽  
Amino Acid Level ◽  
Hla Typing ◽  
Sequencing Data ◽  
Desktop Computer ◽  
Short Read ◽  
Short Read Sequencing ◽  
Hla Genes ◽  
Human Chromosome 6

The HLA gene complex on human chromosome 6 is one of the most polymorphic regions in the human genome and contributes in large part to the diversity of the immune system. Accurate typing of HLA genes with short-read sequencing data has historically been difficult due to the sequence similarity between the polymorphic alleles. Here, we introduce an algorithm, xHLA, that iteratively refines the mapping results at the amino acid level to achieve 99–100% four-digit typing accuracy for both class I and II HLA genes, taking only∼3 min to process a 30× whole-genome BAM file on a desktop computer.

scholarly journals NASA GeneLab RNA-Seq Consensus Pipeline: Standardized Processing of Short-Read RNA-Seq Data

2020 ◽  
Author(s):  
Eliah G. Overbey ◽  
Amanda M. Saravia-Butler ◽  
Zhe Zhang ◽  
Komal S. Rathi ◽  
Homer Fogle ◽  
...  
Keyword(s):  
Expression Profiles ◽  
Gene Expression Profiles ◽  
Rna Seq ◽  
Sequencing Data ◽  
Analysis Pipeline ◽  
Short Read ◽  
Gene Quantification ◽  
Working Groups ◽  
Using Data ◽  
Data Analysis Pipeline

SummaryWith the development of transcriptomic technologies, we are able to quantify precise changes in gene expression profiles from astronauts and other organisms exposed to spaceflight. Members of NASA GeneLab and GeneLab-associated analysis working groups (AWGs) have developed a consensus pipeline for analyzing short-read RNA-sequencing data from spaceflight-associated experiments. The pipeline includes quality control, read trimming, mapping, and gene quantification steps, culminating in the detection of differentially expressed genes. This data analysis pipeline and the results of its execution using data submitted to GeneLab are now all publicly available through the GeneLab database. We present here the full details and rationale for the construction of this pipeline in order to promote transparency, reproducibility and reusability of pipeline data, to provide a template for data processing of future spaceflight-relevant datasets, and to encourage cross-analysis of data from other databases with the data available in GeneLab.

scholarly journals German-wide interlaboratory study compares consistency, accuracy and reproducibility of whole-genome short read sequencing

2020 ◽  
Author(s):  
Laura Uelze ◽  
Maria Borowiak ◽  
Erik Brinks ◽  
Carlus Deneke ◽  
Kerstin Stingl ◽  
...  
Keyword(s):  
Sequence Data ◽  
Bacterial Species ◽  
Quality Parameters ◽  
Data Sets ◽  
Ion Torrent ◽  
Short Read ◽  
Short Read Sequencing ◽  
Safety Research ◽  
Sequencing Platforms ◽  
Sequence Quality

AbstractWe compared the consistency, accuracy and reproducibility of next-generation short read sequencing between ten laboratories involved in food safety (research institutes, state laboratories, universities and companies) from Germany and Austria. Participants were asked to sequence six DNA samples of three bacterial species (Campylobacter jejuni, Listeria monocytogenes and Salmonella enterica) in duplicate, according to their routine in-house sequencing protocol. Four different types of Illumina sequencing platforms (MiSeq, NextSeq, iSeq, NovaSeq) and one Ion Torrent sequencing instrument (S5) were involved in the study. Sequence quality parameters were determined for all data sets and centrally compared between laboratories. SNP / and cgMLST calling were performed to assess the reproducibility of sequence data collected for individual samples. Overall, we found Illumina short read data to be more accurate and consistent than Ion Torrent sequence data, with little variation between the different Illumina instruments. Two laboratories with Illumina instruments submitted sequence data with lower quality, probably due to the use of a library preparation kit, which shows difficulty in sequencing low GC genome regions. Differences in data quality were more evident after assembling short reads into genome assemblies, with Ion Torrent assemblies featuring a great number of allele differences to Illumina assemblies. Clonality of samples was confirmed through SNP calling, which proved to be a more suitable method for an integrated data analysis of Illumina and Ion Torrent data sets, than cgMLST calling.

scholarly journals A benchmarking of human mitochondrial DNA haplogroup classifiers from whole-genome and whole-exome sequence data

2021 ◽  
Author(s):  
Víctor García-Olivares ◽  
Adrián Muñoz-Barrera ◽  
José Miguel Lorenzo-Salazar ◽  
Carlos Zaragoza-Trello ◽  
Luis A. Rubio-Rodríguez ◽  
...  
Keyword(s):  
High Throughput Sequencing ◽  
De Novo ◽  
Sequence Data ◽  
Qualitative Assessment ◽  
Whole Genome ◽  
Third Generation ◽  
Sequencing Data ◽  
Short Read ◽  
Bioinformatic Tools ◽  
Whole Exome

AbstractThe mitochondrial genome (mtDNA) is of interest for a range of fields including evolutionary, forensic, and medical genetics. Human mitogenomes can be classified into evolutionary related haplogroups that provide ancestral information and pedigree relationships. Because of this and the advent of high-throughput sequencing (HTS) technology, there is a diversity of bioinformatic tools for haplogroup classification. We present a benchmarking of the 11 most salient tools for human mtDNA classification using empirical whole-genome (WGS) and whole-exome (WES) short-read sequencing data from 36 unrelated donors. Besides, because of its relevance, we also assess the best performing tool in third-generation long noisy read WGS data obtained with nanopore technology for a subset of the donors. We found that, for short-read WGS, most of the tools exhibit high accuracy for haplogroup classification irrespective of the input file used for the analysis. However, for short-read WES, Haplocheck and MixEmt were the most accurate tools. Based on the performance shown for WGS and WES, and the accompanying qualitative assessment, Haplocheck stands out as the most complete tool. For third-generation HTS data, we also showed that Haplocheck was able to accurately retrieve mtDNA haplogroups for all samples assessed, although only after following assembly-based approaches (either based on a referenced-based assembly or a hybrid de novo assembly). Taken together, our results provide guidance for researchers to select the most suitable tool to conduct the mtDNA analyses from HTS data.

scholarly journals Systematic processing of ribosomal RNA gene amplicon sequencing data

GigaScience ◽  
2019 ◽  
Vol 8 (12) ◽  
Author(s):  
Julien Tremblay ◽  
Etienne Yergeau
Keyword(s):  
Ribosomal Rna ◽  
High Performance ◽  
High Throughput Sequencing ◽  
Sequence Data ◽  
Low Cost ◽  
Marker Gene ◽  
Fine Tuning ◽  
Rrna Gene ◽  
Sequencing Data ◽  
Data Types

Abstract Background With the advent of high-throughput sequencing, microbiology is becoming increasingly data-intensive. Because of its low cost, robust databases, and established bioinformatic workflows, sequencing of 16S/18S/ITS ribosomal RNA (rRNA) gene amplicons, which provides a marker of choice for phylogenetic studies, has become ubiquitous. Many established end-to-end bioinformatic pipelines are available to perform short amplicon sequence data analysis. These pipelines suit a general audience, but few options exist for more specialized users who are experienced in code scripting, Linux-based systems, and high-performance computing (HPC) environments. For such an audience, existing pipelines can be limiting to fully leverage modern HPC capabilities and perform tweaking and optimization operations. Moreover, a wealth of stand-alone software packages that perform specific targeted bioinformatic tasks are increasingly accessible, and finding a way to easily integrate these applications in a pipeline is critical to the evolution of bioinformatic methodologies. Results Here we describe AmpliconTagger, a short rRNA marker gene amplicon pipeline coded in a Python framework that enables fine tuning and integration of virtually any potential rRNA gene amplicon bioinformatic procedure. It is designed to work within an HPC environment, supporting a complex network of job dependencies with a smart-restart mechanism in case of job failure or parameter modifications. As proof of concept, we present end results obtained with AmpliconTagger using 16S, 18S, ITS rRNA short gene amplicons and Pacific Biosciences long-read amplicon data types as input. Conclusions Using a selection of published algorithms for generating operational taxonomic units and amplicon sequence variants and for computing downstream taxonomic summaries and diversity metrics, we demonstrate the performance and versatility of our pipeline for systematic analyses of amplicon sequence data.

scholarly journals A MapReduce Framework for DNA Sequencing Data Processing

2016 ◽  
Vol 4 (4) ◽  
pp. 11 ◽  
Author(s):  
Samy Ghoneimy ◽  
Samir Abou El-Seoud
Keyword(s):  
Data Processing ◽  
Dna Sequencing ◽  
High Performance ◽  
Alternative Methods ◽  
Alignment Algorithm ◽  
Sequencing Data ◽  
Illumina Hiseq ◽  
Data Set ◽  
Short Read ◽  
Short Oligonucleotide

<p class="Els-1storder-head">Genomics and Next Generation Sequencers (NGS) like Illumina Hiseq produce data in the order of ‎‎200 billion base pairs in a single one-week run for a 60x human genome coverage, which ‎requires modern high-throughput experimental technologies that can ‎only be tackled with high performance computing (HPC) and specialized software algorithms called ‎‎“short read aligners”. This paper focuses on the implementation of the DNA sequencing as a set of MapReduce programs that will accept a DNA data set as a FASTQ file and finally generate a VCF (variant call format) file, which has variants for a given DNA data set. In this paper MapReduce/Hadoop along with Burrows-Wheeler Aligner (BWA), Sequence Alignment/Map (SAM) ‎tools, are fully utilized to provide various utilities for manipulating alignments, including sorting, merging, indexing, ‎and generating alignments. The Map-Sort-Reduce process is designed to be suited for a Hadoop framework in ‎which each cluster is a traditional N-node Hadoop cluster to utilize all of the Hadoop features like HDFS, program ‎management and fault tolerance. The Map step performs multiple instances of the short read alignment algorithm ‎‎(BoWTie) that run in parallel in Hadoop. The ordered list of the sequence reads are used as input tuples and the ‎output tuples are the alignments of the short reads. In the Reduce step many parallel instances of the Short ‎Oligonucleotide Analysis Package for SNP (SOAPsnp) algorithm run in the cluster. Input tuples are sorted ‎alignments for a partition and the output tuples are SNP calls. Results are stored via HDFS, and then archived in ‎SOAPsnp format. ‎ The proposed framework enables extremely fast discovering somatic mutations, inferring population genetical ‎parameters, and performing association tests directly based on sequencing data without explicit genotyping or ‎linkage-based imputation. It also demonstrate that this method achieves comparable accuracy to alternative ‎methods for sequencing data processing.‎‎</p><p class="Abstract"><em></em><em><br /></em></p>

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