Improving Metagenomic Classification using Discriminative k-mers from Sequencing Data

Mapping Intimacies ◽

10.1101/2020.02.20.957308 ◽

2020 ◽

Cited By ~ 1

Author(s):

D. Storato ◽

M. Comin

Keyword(s):

Reference Genome ◽

Actual Number ◽

Sequencing Data ◽

Viral Genomes ◽

Link Type ◽

Metagenomic Sample ◽

Reference Genomes ◽

F Measure

AbstractThe major problem when analyzing a metagenomic sample is to taxonomically annotate its reads in order to identify the species they contain. Most of the methods currently available focus on the classification of reads using a set of reference genomes and their k-mers. While in terms of precision these methods have reached percentages of correctness close to perfection, in terms of recall (the actual number of classified reads) the performances fall at around 50%. One of the reasons is the fact that the sequences in a sample can be very different from the corresponding reference genome, e.g. viral genomes are highly mutated. To address this issue, in this paper we study the problem of metagenomic reads classification by improving the reference k-mers library with novel discriminative k-mers from the input sequencing reads. We evaluated the performance in different conditions against several other tools and the results showed an improved F-measure, especially when close reference genomes are not available.Availabilityhttps://github.com/davide92/K2Mem.git

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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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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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Whisper: Read sorting allows robust mapping of sequencing data

10.1101/240358 ◽

2017 ◽

Author(s):

Sebastian Deorowicz ◽

Agnieszka Debudaj-Grabysz ◽

Adam Gudyś ◽

Szymon Grabowski

Keyword(s):

Reference Genome ◽

Variant Calling ◽

Real Data ◽

Supplementary Information ◽

Sequencing Data ◽

Suffix Arrays ◽

Link Type ◽

Mapping Tool ◽

Reverse Complement ◽

Comparable Accuracy

AbstractMotivationMapping reads to a reference genome is often the first step in a sequencing data analysis pipeline. Mistakes made at this computationally challenging stage cannot be recovered easily.ResultsWe present Whisper, an accurate and high-performant mapping tool, based on the idea of sorting reads and then mapping them against suffix arrays for the reference genome and its reverse complement. Employing task and data parallelism as well as storing temporary data on disk result in superior time efficiency at reasonable memory requirements. Whisper excels at large NGS read collections, in particular Illumina reads with typical WGS coverage. The experiments with real data indicate that our solution works in about 15% of the time needed by the well-known Bowtie2 and BWA-MEM tools at a comparable accuracy (validated in variant calling pipeline).AvailabilityWhisper is available for free from https://github.com/refresh-bio/Whisper or http://sun.aei.polsl.pl/REFRESH/Whisper/[email protected] informationSupplementary data are available at publisher Web site.

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Reducing reference bias using multiple population reference genomes

10.1101/2020.03.03.975219 ◽

2020 ◽

Cited By ~ 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 causes 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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Perfect Match Genomic Landscape strategy: Refinement and customization of reference genomes

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2025192118 ◽

2021 ◽

Vol 118 (14) ◽

pp. e2025192118

Author(s):

Kim Palacios-Flores ◽

Jair García-Sotelo ◽

Alejandra Castillo ◽

Carina Uribe ◽

Lucía Morales ◽

...

Keyword(s):

Reference Genome ◽

Pyrococcus Furiosus ◽

Perfect Match ◽

Proof Of Concept ◽

Methanococcus Maripaludis ◽

Viral Genomes ◽

E Coli ◽

Genomic Landscape ◽

Domains Of Life ◽

Reference Genomes

When addressing a genomic question, having a reliable and adequate reference genome is of utmost importance. This drives the necessity to refine and customize reference genomes (RGs). Our laboratory has recently developed a strategy, the Perfect Match Genomic Landscape (PMGL), to detect variation between genomes [K. Palacios-Flores et al.. Genetics 208, 1631–1641 (2018)]. The PMGL is precise and sensitive and, in contrast to most currently used algorithms, is nonstatistical in nature. Here we demonstrate the power of PMGL to refine and customize RGs. As a proof-of-concept, we refined different versions of the Saccharomyces cerevisiae RG. We applied the automatic PMGL pipeline to refine the genomes of microorganisms belonging to the three domains of life: the archaea Methanococcus maripaludis and Pyrococcus furiosus; the bacteria Escherichia coli, Staphylococcus aureus, and Bacillus subtilis; and the eukarya Schizosaccharomyces pombe, Aspergillus oryzae, and several strains of Saccharomyces paradoxus. We analyzed the reference genome of the virus SARS-CoV-2 and previously published viral genomes from patients’ samples with COVID-19. We performed a mutation-accumulation experiment in E. coli and show that the PMGL strategy can detect specific mutations generated at any desired step of the whole procedure. We propose that PMGL can be used as a final step for the refinement and customization of any haploid genome, independently of the strategies and algorithms used in its assembly.

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Novel functional sequences uncovered through a bovine multiassembly graph

Proceedings of the National Academy of Sciences ◽

10.1073/pnas.2101056118 ◽

2021 ◽

Vol 118 (20) ◽

pp. e2101056118

Author(s):

Danang Crysnanto ◽

Alexander S. Leonard ◽

Zih-Hua Fang ◽

Hubert Pausch

Keyword(s):

Genetic Diversity ◽

Reference Genome ◽

Bos Taurus ◽

Whole Genome Sequencing Data ◽

Whole Genome ◽

Sequencing Data ◽

Reference Allele ◽

Genomic Analyses ◽

Reference Quality ◽

Reference Genomes

Many genomic analyses start by aligning sequencing reads to a linear reference genome. However, linear reference genomes are imperfect, lacking millions of bases of unknown relevance and are unable to reflect the genetic diversity of populations. This makes reference-guided methods susceptible to reference-allele bias. To overcome such limitations, we build a pangenome from six reference-quality assemblies from taurine and indicine cattle as well as yak. The pangenome contains an additional 70,329,827 bases compared to the Bos taurus reference genome. Our multiassembly approach reveals 30 and 10.1 million bases private to yak and indicine cattle, respectively, and between 3.3 and 4.4 million bases unique to each taurine assembly. Utilizing transcriptomes from 56 cattle, we show that these nonreference sequences encode transcripts that hitherto remained undetected from the B. taurus reference genome. We uncover genes, primarily encoding proteins contributing to immune response and pathogen-mediated immunomodulation, differentially expressed between Mycobacterium bovis–infected and noninfected cattle that are also undetectable in the B. taurus reference genome. Using whole-genome sequencing data of cattle from five breeds, we show that reads which were previously misaligned against the Bos taurus reference genome now align accurately to the pangenome sequences. This enables us to discover 83,250 polymorphic sites that segregate within and between breeds of cattle and capture genetic differentiation across breeds. Our work makes a so-far unused source of variation amenable to genetic investigations and provides methods and a framework for establishing and exploiting a more diverse reference genome.

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VirStrain: a strain identification tool for RNA viruses

10.1101/2020.12.21.423722 ◽

2020 ◽

Author(s):

Herui Liao ◽

Dehan Cai ◽

Yanni Sun

Keyword(s):

Rna Viruses ◽

Strain Identification ◽

Sequencing Data ◽

Viral Genomes ◽

Benchmark Datasets ◽

Multiple Strains ◽

Reference Genomes ◽

Geographical Locations ◽

Level Analysis ◽

Identification Tool

1AbstractGenome epidemiology, which uses genomic data to analyze the source and spread of infectious diseases, provides important information beyond interview-based methods. Given fast accumulation of sequenced viral genomes, a basic need in genome epidemiology is to identify which reference genomes are identical or closest to the ones in a sequenced sample. Then the associated metadata such as the geographical locations can be utilized to infer the transmission network. In this work, we deliver VirStrain, a fast and accurate tool for conducting strain-level analysis from short reads. By using a greedy covering algorithm, we are able to derive unique k-mer combinations for highly similar reference genomes. VirStrain is able to detect the most possible strain and also multiple strains that may simultaneously infect the same host. We tested VirStrain on three types of RNA viruses whose reference genomes have different similarity distributions. For each types of virus, we assessed VirStrain across multiple benchmark datasets of different properties and complexity. The experimental results on both simulated and real sequencing data show that VirStrain outperforms other strain identification tools.

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Reference genome and annotation updates lead to contradictory prognostic predictions in gene expression signatures: a case study of resected stage I lung adenocarcinoma

Briefings in Bioinformatics ◽

10.1093/bib/bbaa081 ◽

2020 ◽

Author(s):

Zheyang Zhang ◽

Sainan Zhang ◽

Xin Li ◽

Zhangxiang Zhao ◽

Changjing Chen ◽

...

Keyword(s):

Lung Adenocarcinoma ◽

Rna Sequencing ◽

Reference Genome ◽

Low Cost ◽

Risk Classification ◽

Stage I ◽

The Cancer Genome Atlas ◽

Sequencing Data ◽

Public Data ◽

Reference Genomes

Abstract RNA-sequencing enables accurate and low-cost transcriptome-wide detection. However, expression estimates vary as reference genomes and gene annotations are updated, confounding existing expression-based prognostic signatures. Herein, prognostic 9-gene pair signature (GPS) was applied to 197 patients with stage I lung adenocarcinoma derived from previous and latest data from The Cancer Genome Atlas (TCGA) processed with different reference genomes and annotations. For 9-GPS, 6.6% of patients exhibited discordant risk classifications between the two TCGA versions. Similar results were observed for other prognostic signatures, including IRGPI, 15-gene and ORACLE. We found that conflicting annotations for gene length and overlap were the major cause of their discordant risk classification. Therefore, we constructed a prognostic 40-GPS based on stable genes across GENCODE v20-v30 and validated it using public data of 471 stage I samples (log-rank P < 0.0010). Risk classification was still stable in RNA-sequencing data processed with the newest GENCODE v32 versus GENCODE v20–v30. Specifically, 40-GPS could predict survival for 30 stage I samples with formalin-fixed paraffin-embedded tissues (log-rank P = 0.0177). In conclusion, this method overcomes the vulnerability of existing prognostic signatures due to reference genome and annotation updates. 40-GPS may offer individualized clinical applications due to its prognostic accuracy and classification stability.

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Accurate sequence variant genotyping in cattle using variation-aware genome graphs

10.1101/460345 ◽

2018 ◽

Cited By ~ 1

Author(s):

Danang Crysnanto ◽

Christine Wurmser ◽

Hubert Pausch

Keyword(s):

Dna Sequences ◽

Reference Genome ◽

Sequence Data ◽

Sequence Variant ◽

Sequence Variants ◽

Sequencing Data ◽

Reference Allele ◽

Reference Genomes ◽

Genome Graph ◽

Genotype Concordance

Background: The genotyping of sequence variants typically involves as a first step the alignment of sequencing reads to a linear reference genome. Because a linear reference genome represents only a small fraction of sequence variation within a species, reference allele bias may occur at highly polymorphic or diverged regions of the genome. Graph-based methods facilitate to compare sequencing reads to a variation-aware genome graph that incorporates non-redundant DNA sequences that segregate within a species. We compared accuracy and sensitivity of graph-based sequence variant genotyping using the Graphtyper software to two widely used methods, i.e., GATK and SAMtools, that rely on linear reference genomes using whole-genomes sequencing data of 49 Original Braunvieh cattle. Results: We discovered 21,140,196, 20,262,913 and 20,668,459 polymorphic sites using GATK, Graphtyper, and SAMtools, respectively. Comparisons between sequence variant and microarray-derived genotypes showed that Graphtyper outperformed both GATK and SAMtools in terms of genotype concordance, non-reference sensitivity, and non-reference discrepancy. The sequence variant genotypes that were obtained using Graphtyper had the lowest number of mendelian inconsistencies for both SNPs and indels in nine sire-son pairs with sequence data. Genotype phasing and imputation using the Beagle software improved the quality of the sequence variant genotypes for all tools evaluated particularly for animals that have been sequenced at low coverage. Following imputation, the concordance between sequence- and microarray-derived genotypes was almost identical for the three methods evaluated, i.e., 99.32, 99.46, and 99.24 % for GATK, Graphtyper, and SAMtools, respectively. Variant filtration based on commonly used criteria improved the genotype concordance slightly but it also decreased sensitivity. Graphtyper required considerably more computing resources than SAMtools but it required less than GATK. Conclusions: Sequence variant genotyping using Graphtyper is accurate, sensitive and computationally feasible in cattle. Graph-based methods enable sequence variant genotyping from variation-aware reference genomes that may incorporate cohort-specific sequence variants which is not possible with the current implementations of state-of-the-art methods that rely on linear reference genomes.

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Using QC-Blind for quality control and contamination screening of bacteria DNA sequencing data without reference genome

10.1101/438655 ◽

2018 ◽

Author(s):

Wang Xi ◽

Yan Gao ◽

Zhangyu Cheng ◽

Chaoyun Chen ◽

Maozhen Han ◽

...

Keyword(s):

Quality Control ◽

Reference Genome ◽

Bacterial Species ◽

Real Life ◽

High Specificity ◽

Limited Information ◽

Target Species ◽

Sequencing Data ◽

Reference Genomes

ABSTRACTQuality control in next generation sequencing has become increasingly important as the technique becomes widely used. Tools have been developed for filtering possible contaminants in the sequencing data of species with known reference genome. Unfortunately, reference genomes for all the species involved, including the contaminants, are required for these tools to work. This precludes many real-life samples that have no information about the complete genome of the target species, and are contaminated with unknown microbial species.In this work we propose QC-Blind, a novel quality control pipeline for removing contaminants without any use of reference genomes. The pipeline requires only very little information from the marker genes of the target species. The entire pipeline consists of unsupervised read assembly, contig binning, read clustering and marker gene assignment.When evaluated onin silico,ab initioandin vivodatasets, QC-Blind proved effective in removing unknown contaminants with high specificity and accuracy, while preserving most of the genomic information of the target bacterial species. Therefore, QC-Blind could serve well in situations where limited information is available for both target and contamination species.IMPORTANCEAt present, many sequencing projects are still performed on potentially contaminated samples, which bring into question their accuracies. However, current reference-based quality control method are limited as they need either the genome of target species or contaminations. In this work we propose QC-Blind, a novel quality control pipeline for removing contaminants without any use of reference genomes. When evaluated onin silico,ab initioandin vivodatasets, QC-Blind proved effective in removing unknown contaminants with high specificity and accuracy, while preserving most of the genomic information of the target bacterial species. Therefore, QC-Blind is suitable for real-life samples where limited information is available for both target and contamination species.

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