BWA-mem is not the best aligner for ancient DNA short reads.

Mapping Intimacies ◽

10.1101/2021.08.02.454401 ◽

2021 ◽

Author(s):

Adrien Oliva ◽

Raymond Tobler ◽

Bastien Llamas ◽

Yassine Souilmi

Keyword(s):

Dna Sequencing ◽

Ancient Dna ◽

Performance Metrics ◽

Sequencing Data ◽

Current Standard ◽

Short Reads ◽

Mapping Software ◽

Reference Bias

Xu and colleagues (Xu et al., 2021) recently suggested a new parameterisation of BWA-mem (Li, 2013) as an alternative to the current standard BWA-aln (Li and Durbin, 2009) to process ancient DNA sequencing data. The authors tested several combinations of the -k and -r parameters to optimise BWA-mem performance with degraded and contaminated ancient DNA samples. They report that using BWA-mem with -k 19 -r 2.5 parameters results in a mapping efficiency comparable to BWA-aln with -I 1024 -n 0.03 (i.e. a derivation of the standard parameters used in ancient DNA studies; (Schubert et al., 2012)), while achieving significantly faster run times. We recently performed a systematic benchmark of four mapping software (i.e. BWA-aln, BWA-mem, NovoAlign (http://www.novocraft.com/products/novoalign), and Bowtie2 (Langmead and Salzberg, 2012) for ancient DNA sequencing data and quantified their precision, accuracy, specificity, and impact on reference bias (Oliva et al., 2021). Notably, while multiple parameterisations were tested for BWA-aln, NovoAlign, and Bowtie2, we only tested BWA-mem with default parameters. Here, we use the alignment performance metrics from Oliva et al. to directly compare the recommended BWA-mem parameterisation reported in Xu et al. with the best performing alignment methods determined in the Oliva et al. benchmarks, and we make recommendations based on the results.

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Systematic benchmark of ancient DNA read mapping

Briefings in Bioinformatics ◽

10.1093/bib/bbab076 ◽

2021 ◽

Author(s):

Adrien Oliva ◽

Raymond Tobler ◽

Alan Cooper ◽

Bastien Llamas ◽

Yassine Souilmi

Keyword(s):

Ancient Dna ◽

Dna Sequences ◽

Population Genetic ◽

Reference Genome ◽

Population Data ◽

Human Populations ◽

Current Standard ◽

Read Mapping ◽

Reference Bias ◽

The Impact

Abstract The current standard practice for assembling individual genomes involves mapping millions of short DNA sequences (also known as DNA ‘reads’) against a pre-constructed reference genome. Mapping vast amounts of short reads in a timely manner is a computationally challenging task that inevitably produces artefacts, including biases against alleles not found in the reference genome. This reference bias and other mapping artefacts are expected to be exacerbated in ancient DNA (aDNA) studies, which rely on the analysis of low quantities of damaged and very short DNA fragments (~30–80 bp). Nevertheless, the current gold-standard mapping strategies for aDNA studies have effectively remained unchanged for nearly a decade, during which time new software has emerged. In this study, we used simulated aDNA reads from three different human populations to benchmark the performance of 30 distinct mapping strategies implemented across four different read mapping software—BWA-aln, BWA-mem, NovoAlign and Bowtie2—and quantified the impact of reference bias in downstream population genetic analyses. We show that specific NovoAlign, BWA-aln and BWA-mem parameterizations achieve high mapping precision with low levels of reference bias, particularly after filtering out reads with low mapping qualities. However, unbiased NovoAlign results required the use of an IUPAC reference genome. While relevant only to aDNA projects where reference population data are available, the benefit of using an IUPAC reference demonstrates the value of incorporating population genetic information into the aDNA mapping process, echoing recent results based on graph genome representations.

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Analysis of High-Throughput Ancient DNA Sequencing Data

Methods in Molecular Biology - Ancient DNA ◽

10.1007/978-1-61779-516-9_23 ◽

2011 ◽

pp. 197-228 ◽

Cited By ~ 108

Author(s):

Martin Kircher

Keyword(s):

Dna Sequencing ◽

High Throughput ◽

Ancient Dna ◽

Sequencing Data

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Distinguishing linear and branched evolution given single-cell DNA sequencing data of tumors

Algorithms for Molecular Biology ◽

10.1186/s13015-021-00194-5 ◽

2021 ◽

Vol 16 (1) ◽

Author(s):

Leah L. Weber ◽

Mohammed El-Kebir

Keyword(s):

Dna Sequencing ◽

Single Cell ◽

Evolutionary Process ◽

Treatment Decision ◽

Real Data ◽

Current Data ◽

Fast Method ◽

Sequencing Data ◽

Evolutionary Trajectory ◽

Cancer Types

Abstract Background Cancer arises from an evolutionary process where somatic mutations give rise to clonal expansions. Reconstructing this evolutionary process is useful for treatment decision-making as well as understanding evolutionary patterns across patients and cancer types. In particular, classifying a tumor’s evolutionary process as either linear or branched and understanding what cancer types and which patients have each of these trajectories could provide useful insights for both clinicians and researchers. While comprehensive cancer phylogeny inference from single-cell DNA sequencing data is challenging due to limitations with current sequencing technology and the complexity of the resulting problem, current data might provide sufficient signal to accurately classify a tumor’s evolutionary history as either linear or branched. Results We introduce the Linear Perfect Phylogeny Flipping (LPPF) problem as a means of testing two alternative hypotheses for the pattern of evolution, which we prove to be NP-hard. We develop Phyolin, which uses constraint programming to solve the LPPF problem. Through both in silico experiments and real data application, we demonstrate the performance of our method, outperforming a competing machine learning approach. Conclusion Phyolin is an accurate, easy to use and fast method for classifying an evolutionary trajectory as linear or branched given a tumor’s single-cell DNA sequencing data.

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A Streamlined Approach to Pathway Analysis from RNA-Sequencing Data

Methods and Protocols ◽

10.3390/mps4010021 ◽

2021 ◽

Vol 4 (1) ◽

pp. 21

Author(s):

Austin Bow

Keyword(s):

Rna Sequencing ◽

Pathway Analysis ◽

Valuable Insight ◽

Sequencing Data ◽

Analytical Technique ◽

Mapping Software ◽

Formidable Challenge ◽

Daunting Task ◽

Software Platforms ◽

Time Required

The reduction in costs associated with performing RNA-sequencing has driven an increase in the application of this analytical technique; however, restrictive factors associated with this tool have now shifted from budgetary constraints to time required for data processing. The sheer scale of the raw data produced can present a formidable challenge for researchers aiming to glean vital information about samples. Though many of the companies that perform RNA-sequencing provide a basic report for the submitted samples, this may not adequately capture particular pathways of interest for sample comparisons. To further assess these data, it can therefore be necessary to utilize various enrichment and mapping software platforms to highlight specific relations. With the wide array of these software platforms available, this can also present a daunting task. The methodology described herein aims to enable researchers new to handling RNA-sequencing data with a streamlined approach to pathway analysis. Additionally, the implemented software platforms are readily available and free to utilize, making this approach viable, even for restrictive budgets. The resulting tables and nodal networks will provide valuable insight into samples and can be used to generate high-quality graphics for publications and presentations.

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Reference flow: reducing reference bias using multiple population genomes

Genome Biology ◽

10.1186/s13059-020-02229-3 ◽

2021 ◽

Vol 22 (1) ◽

Author(s):

Nae-Chyun Chen ◽

Brad Solomon ◽

Taher Mun ◽

Sheila Iyer ◽

Ben Langmead

Keyword(s):

Genetic Variation ◽

Reference Genome ◽

Alignment Method ◽

Sequencing Data ◽

Computational Overhead ◽

Reference Flow ◽

Multiple Population ◽

Reference Bias ◽

Flow Alignment ◽

Reference Genomes

AbstractMost sequencing data analyses start by aligning sequencing reads to a linear reference genome, but failure to account for genetic variation leads to reference bias and confounding of results downstream. Other approaches replace the linear reference with structures like graphs that can include genetic variation, incurring major computational overhead. We propose the reference flow alignment method that uses multiple population reference genomes to improve alignment accuracy and reduce reference bias. Compared to the graph aligner vg, reference flow achieves a similar level of accuracy and bias avoidance but with 14% of the memory footprint and 5.5 times the speed.

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Using single-cell cytometry to illustrate integrated multi-perspective evaluation of clustering algorithms using Pareto fronts

Bioinformatics ◽

10.1093/bioinformatics/btab038 ◽

2021 ◽

Author(s):

Givanna H Putri ◽

Irena Koprinska ◽

Thomas M Ashhurst ◽

Nicholas J C King ◽

Mark N Read

Keyword(s):

Single Cell ◽

Performance Metrics ◽

Clustering Algorithms ◽

Latin Hypercube Sampling ◽

Supplementary Information ◽

Sequencing Data ◽

Evaluation Protocol ◽

Benchmark Datasets ◽

Pareto Fronts ◽

Parameter Values

Abstract Motivation Many ‘automated gating’ algorithms now exist to cluster cytometry and single-cell sequencing data into discrete populations. Comparative algorithm evaluations on benchmark datasets rely either on a single performance metric, or a few metrics considered independently of one another. However, single metrics emphasize different aspects of clustering performance and do not rank clustering solutions in the same order. This underlies the lack of consensus between comparative studies regarding optimal clustering algorithms and undermines the translatability of results onto other non-benchmark datasets. Results We propose the Pareto fronts framework as an integrative evaluation protocol, wherein individual metrics are instead leveraged as complementary perspectives. Judged superior are algorithms that provide the best trade-off between the multiple metrics considered simultaneously. This yields a more comprehensive and complete view of clustering performance. Moreover, by broadly and systematically sampling algorithm parameter values using the Latin Hypercube sampling method, our evaluation protocol minimizes (un)fortunate parameter value selections as confounding factors. Furthermore, it reveals how meticulously each algorithm must be tuned in order to obtain good results, vital knowledge for users with novel data. We exemplify the protocol by conducting a comparative study between three clustering algorithms (ChronoClust, FlowSOM and Phenograph) using four common performance metrics applied across four cytometry benchmark datasets. To our knowledge, this is the first time Pareto fronts have been used to evaluate the performance of clustering algorithms in any application domain. Availability and implementation Implementation of our Pareto front methodology and all scripts and datasets to reproduce this article are available at https://github.com/ghar1821/ParetoBench. Supplementary information Supplementary data are available at Bioinformatics online.

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Putatively adaptive genetic variation in the giant California sea cucumber ( Parastichopus californicus ) as revealed by environmental association analysis of restriction‐site associated DNA sequencing data

Molecular Ecology ◽

10.1111/mec.14942 ◽

2018 ◽

Vol 27 (24) ◽

pp. 5035-5048 ◽

Cited By ~ 13

Author(s):

Amanda Xuereb ◽

Christopher M. Kimber ◽

Janelle M. R. Curtis ◽

Louis Bernatchez ◽

Marie‐Josée Fortin

Keyword(s):

Genetic Variation ◽

Dna Sequencing ◽

Association Analysis ◽

Sea Cucumber ◽

Restriction Site ◽

Sequencing Data ◽

Adaptive Genetic Variation ◽

Environmental Association

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Splicing Express: a software suite for alternative splicing analysis using next-generation sequencing data

PeerJ ◽

10.7717/peerj.1419 ◽

2015 ◽

Vol 3 ◽

pp. e1419 ◽

Cited By ~ 6

Author(s):

Jose E. Kroll ◽

Jihoon Kim ◽

Lucila Ohno-Machado ◽

Sandro J. de Souza

Keyword(s):

Alternative Splicing ◽

Dna Sequencing ◽

Next Generation Sequencing Data ◽

Next Generation ◽

Sequencing Data ◽

Software Suite ◽

Next Generation Dna Sequencing ◽

Sequencing Technologies ◽

Body Map ◽

Sequencing Platforms

Motivation.Alternative splicing events (ASEs) are prevalent in the transcriptome of eukaryotic species and are known to influence many biological phenomena. The identification and quantification of these events are crucial for a better understanding of biological processes. Next-generation DNA sequencing technologies have allowed deep characterization of transcriptomes and made it possible to address these issues. ASEs analysis, however, represents a challenging task especially when many different samples need to be compared. Some popular tools for the analysis of ASEs are known to report thousands of events without annotations and/or graphical representations. A new tool for the identification and visualization of ASEs is here described, which can be used by biologists without a solid bioinformatics background.Results.A software suite namedSplicing Expresswas created to perform ASEs analysis from transcriptome sequencing data derived from next-generation DNA sequencing platforms. Its major goal is to serve the needs of biomedical researchers who do not have bioinformatics skills.Splicing Expressperforms automatic annotation of transcriptome data (GTF files) using gene coordinates available from the UCSC genome browser and allows the analysis of data from all available species. The identification of ASEs is done by a known algorithm previously implemented in another tool namedSplooce. As a final result,Splicing Expresscreates a set of HTML files composed of graphics and tables designed to describe the expression profile of ASEs among all analyzed samples. By using RNA-Seq data from the Illumina Human Body Map and the Rat Body Map, we show thatSplicing Expressis able to perform all tasks in a straightforward way, identifying well-known specific events.Availability and Implementation.Splicing Expressis written in Perl and is suitable to run only in UNIX-like systems. More details can be found at:http://www.bioinformatics-brazil.org/splicingexpress.

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