Comparison of Sequencing Utility Programs

High throughput sequencing (HTS) has resulted in extreme growth rates of sequencing data. At our lab, we generate terabytes of data every day. It is usually seen as required for data output to be “cleaned” and processed in various ways prior to use for common tasks such as variant calling, expression quantification and assembly. Two common tasks associated with HTS are adapter trimming and paired-end joining. I have developed two tools at Expression Analysis, Inc. to address these common tasks. The names of these programs are fastq-mcf and fastq-join. I compared the performance of these tools to similar open-source utilities, both in terms of resource efficiency, and effectiveness.

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Accurate fetal variant calling in the presence of maternal cell contamination

10.1101/552414 ◽

2019 ◽

Cited By ~ 1

Author(s):

Elena Nabieva ◽

Satyarth Mishra Sharma ◽

Yermek Kapushev ◽

Sofya K. Garushyants ◽

Anna V. Fedotova ◽

...

Keyword(s):

High Throughput ◽

High Throughput Sequencing ◽

Chorionic Villus ◽

Genetic Diagnosis ◽

Variant Calling ◽

Data Availability ◽

Training Data ◽

Sequencing Data ◽

Maternal Cell ◽

Fetal Dna

AbstractHigh-throughput sequencing of fetal DNA is a promising and increasingly common method for the discovery of all (or all coding) genetic variants in the fetus, either as part of prenatal screening or diagnosis, or for genetic diagnosis of spontaneous abortions. In many cases, the fetal DNA (from chorionic villi, amniotic fluid, or abortive tissue) can be contaminated with maternal cells, resulting in the mixture of fetal and maternal DNA. This maternal cell contamination (MCC) undermines the assumption, made by traditional variant callers, that each allele in a heterozygous site is covered, on average, by 50% of the reads, and therefore can lead to erroneous genotype calls. We present a panel of methods for reducing the genotyping error in the presence of MCC. All methods start with the output of GATK HaplotypeCaller on the sequencing data for the (contaminated) fetal sample and both of its parents, and additionally rely on information about the MCC fraction (which itself is readily estimated from the high-throughput sequencing data). The first of these methods uses a Bayesian probabilistic model to correct the fetal genotype calls produced by MCC-unaware HaplotypeCaller. The other two methods “learn” the genotype-correction model from examples. We use simulated contaminated fetal data to train and test the models. Using the test sets, we show that all three methods lead to substantially improved accuracy when compared with the original MCC-unaware HaplotypeCaller calls. We then apply the best-performing method to three chorionic villus samples from spontaneously terminated pregnancies.Code and training data availabilityhttps://github.com/bazykinlab/ML-maternal-cell-contamination

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Comparison of sequencing data processing pipelines and application to underrepresented African human populations

BMC Bioinformatics ◽

10.1186/s12859-021-04407-x ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Gwenna Breton ◽

Anna C. V. Johansson ◽

Per Sjödin ◽

Carina M. Schlebusch ◽

Mattias Jakobsson

Keyword(s):

Best Practices ◽

High Throughput ◽

High Throughput Sequencing ◽

Variant Calling ◽

Human Populations ◽

Sequencing Data ◽

High Coverage ◽

Individual Level ◽

Bioinformatic Tools ◽

The Individual

Abstract Background Population genetic studies of humans make increasing use of high-throughput sequencing in order to capture diversity in an unbiased way. There is an abundance of sequencing technologies, bioinformatic tools and the available genomes are increasing in number. Studies have evaluated and compared some of these technologies and tools, such as the Genome Analysis Toolkit (GATK) and its “Best Practices” bioinformatic pipelines. However, studies often focus on a few genomes of Eurasian origin in order to detect technical issues. We instead surveyed the use of the GATK tools and established a pipeline for processing high coverage full genomes from a diverse set of populations, including Sub-Saharan African groups, in order to reveal challenges from human diversity and stratification. Results We surveyed 29 studies using high-throughput sequencing data, and compared their strategies for data pre-processing and variant calling. We found that processing of data is very variable across studies and that the GATK “Best Practices” are seldom followed strictly. We then compared three versions of a GATK pipeline, differing in the inclusion of an indel realignment step and with a modification of the base quality score recalibration step. We applied the pipelines on a diverse set of 28 individuals. We compared the pipelines in terms of count of called variants and overlap of the callsets. We found that the pipelines resulted in similar callsets, in particular after callset filtering. We also ran one of the pipelines on a larger dataset of 179 individuals. We noted that including more individuals at the joint genotyping step resulted in different counts of variants. At the individual level, we observed that the average genome coverage was correlated to the number of variants called. Conclusions We conclude that applying the GATK “Best Practices” pipeline, including their recommended reference datasets, to underrepresented populations does not lead to a decrease in the number of called variants compared to alternative pipelines. We recommend to aim for coverage of > 30X if identifying most variants is important, and to work with large sample sizes at the variant calling stage, also for underrepresented individuals and populations.

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iSVP: an integrated structural variant calling pipeline from high-throughput sequencing data

BMC Systems Biology ◽

10.1186/1752-0509-7-s6-s8 ◽

2013 ◽

Vol 7 (Suppl 6) ◽

pp. S8 ◽

Cited By ~ 21

Author(s):

Takahiro Mimori ◽

Naoki Nariai ◽

Kaname Kojima ◽

Mamoru Takahashi ◽

Akira Ono ◽

...

Keyword(s):

High Throughput ◽

High Throughput Sequencing ◽

Variant Calling ◽

Sequencing Data ◽

Structural Variant ◽

High Throughput Sequencing Data

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Bazam: A rapid method for read extraction and realignment of high throughput sequencing data

10.1101/433003 ◽

2018 ◽

Cited By ~ 1

Author(s):

Simon P Sadedin ◽

Alicia Oshlack

Keyword(s):

High Throughput ◽

High Throughput Sequencing ◽

Variant Calling ◽

Genomic Data ◽

Selective Extraction ◽

Sequencing Data ◽

High Throughput Sequencing Data ◽

Time Required ◽

Genomic Regions ◽

Reference Genomes

AbstractBackgroundAs costs of high throughput sequencing have fallen, we are seeing vast quantities of short read genomic data being generated. Often, the data is exchanged and stored as aligned reads, which provides high compression and convenient access for many analyses. However, aligned data becomes outdated as new reference genomes and alignment methods become available. Moreover, some applications cannot utilise pre-aligned reads at all, necessitating conversion back to raw format (FASTQ) before they can be used. In both cases, the process of extraction and realignment is expensive and time consuming.FindingsWe describe Bazam, a tool that efficiently extracts the original paired FASTQ from reads stored in aligned form (BAM or CRAM format). Bazam extracts reads in a format that directly allows realignment with popular aligners with high concurrency. Through eliminating steps and increasing the accessible concurrency, Bazam facilitates up to a 90% reduction in the time required for realignment compared to standard methods. Bazam can support selective extraction of read pairs from focused genomic regions, further increasing efficiency for targeted analyses. Bazam is additionally suitable as a base for other applications that require efficient paired read information, such as quality control, structural variant calling and alignment comparison.ConclusionsBazam offers significant improvements for users needing to realign genomic data.

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Faculty Opinions recommendation of Coalescent Inference Using Serially Sampled, High-Throughput Sequencing Data from Intrahost HIV Infection.

Faculty Opinions – Post-Publication Peer Review of the Biomedical Literature ◽

10.3410/f.726132071.793531014 ◽

2017 ◽

Author(s):

Sarah Rowland-Jones ◽

Sophie Andrews

Keyword(s):

Hiv Infection ◽

High Throughput ◽

High Throughput Sequencing ◽

Sequencing Data ◽

High Throughput Sequencing Data

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BlindCall: ultra-fast base-calling of high-throughput sequencing data by blind deconvolution

Bioinformatics ◽

10.1093/bioinformatics/btu010 ◽

2014 ◽

Vol 30 (9) ◽

pp. 1214-1219 ◽

Cited By ~ 6

Author(s):

C. Ye ◽

C. Hsiao ◽

H. Corrada Bravo

Keyword(s):

High Throughput ◽

High Throughput Sequencing ◽

Blind Deconvolution ◽

Sequencing Data ◽

Base Calling ◽

High Throughput Sequencing Data

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Increased yields of duplex sequencing data by a series of quality control tools

NAR Genomics and Bioinformatics ◽

10.1093/nargab/lqab002 ◽

2021 ◽

Vol 3 (1) ◽

Author(s):

Gundula Povysil ◽

Monika Heinzl ◽

Renato Salazar ◽

Nicholas Stoler ◽

Anton Nekrutenko ◽

...

Keyword(s):

Low Frequency ◽

Variant Calling ◽

Data Loss ◽

Sequencing Data ◽

Bioinformatics Pipeline ◽

Consensus Sequences ◽

Sequencing Errors ◽

Data Output ◽

Reverse Strand ◽

Duplex Sequencing

Abstract Duplex sequencing is currently the most reliable method to identify ultra-low frequency DNA variants by grouping sequence reads derived from the same DNA molecule into families with information on the forward and reverse strand. However, only a small proportion of reads are assembled into duplex consensus sequences (DCS), and reads with potentially valuable information are discarded at different steps of the bioinformatics pipeline, especially reads without a family. We developed a bioinformatics toolset that analyses the tag and family composition with the purpose to understand data loss and implement modifications to maximize the data output for the variant calling. Specifically, our tools show that tags contain polymerase chain reaction and sequencing errors that contribute to data loss and lower DCS yields. Our tools also identified chimeras, which likely reflect barcode collisions. Finally, we also developed a tool that re-examines variant calls from raw reads and provides different summary data that categorizes the confidence level of a variant call by a tier-based system. With this tool, we can include reads without a family and check the reliability of the call, that increases substantially the sequencing depth for variant calling, a particular important advantage for low-input samples or low-coverage regions.

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Population-specific genome graphs improve high-throughput sequencing data analysis: A case study on the Pan-African genome

10.1101/2021.03.19.436173 ◽

2021 ◽

Author(s):

H. Serhat Tetikol ◽

Kubra Narci ◽

Deniz Turgut ◽

Gungor Budak ◽

Ozem Kalay ◽

...

Keyword(s):

High Throughput Sequencing ◽

Information Overload ◽

African Ancestry ◽

Sample Selection ◽

Variant Calling ◽

Population Diversity ◽

Human Populations ◽

Sequencing Data ◽

Bioinformatics Pipeline ◽

Graph Augmentation

ABSTRACTGraph-based genome reference representations have seen significant development, motivated by the inadequacy of the current human genome reference for capturing the diverse genetic information from different human populations and its inability to maintain the same level of accuracy for non-European ancestries. While there have been many efforts to develop computationally efficient graph-based bioinformatics toolkits, how to curate genomic variants and subsequently construct genome graphs remains an understudied problem that inevitably determines the effectiveness of the end-to-end bioinformatics pipeline. In this study, we discuss major obstacles encountered during graph construction and propose methods for sample selection based on population diversity, graph augmentation with structural variants and resolution of graph reference ambiguity caused by information overload. Moreover, we present the case for iteratively augmenting tailored genome graphs for targeted populations and test the proposed approach on the whole-genome samples of African ancestry. Our results show that, as more representative alternatives to linear or generic graph references, population-specific graphs can achieve significantly lower read mapping errors, increased variant calling sensitivity and provide the improvements of joint variant calling without the need of computationally intensive post-processing steps.

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Great differences in performance and outcome of high-throughput sequencing data analysis platforms for fungal metabarcoding

MycoKeys ◽

10.3897/mycokeys.39.28109 ◽

2018 ◽

Vol 39 ◽

pp. 29-40 ◽

Cited By ~ 21

Author(s):

Sten Anslan ◽

R. Henrik Nilsson ◽

Christian Wurzbacher ◽

Petr Baldrian ◽

Leho Tedersoo ◽

...

Keyword(s):

High Throughput ◽

High Throughput Sequencing ◽

Computation Time ◽

Potential Effect ◽

Data Sets ◽

Sequencing Data ◽

Operational Taxonomic Units ◽

High Throughput Sequencing Data ◽

Recent Developments

Along with recent developments in high-throughput sequencing (HTS) technologies and thus fast accumulation of HTS data, there has been a growing need and interest for developing tools for HTS data processing and communication. In particular, a number of bioinformatics tools have been designed for analysing metabarcoding data, each with specific features, assumptions and outputs. To evaluate the potential effect of the application of different bioinformatics workflow on the results, we compared the performance of different analysis platforms on two contrasting high-throughput sequencing data sets. Our analysis revealed that the computation time, quality of error filtering and hence output of specific bioinformatics process largely depends on the platform used. Our results show that none of the bioinformatics workflows appears to perfectly filter out the accumulated errors and generate Operational Taxonomic Units, although PipeCraft, LotuS and PIPITS perform better than QIIME2 and Galaxy for the tested fungal amplicon dataset. We conclude that the output of each platform requires manual validation of the OTUs by examining the taxonomy assignment values.

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