A Fast Approximate Algorithm for Mapping Long Reads to Large Reference Databases

ModDotPlot-Rapid and interactive visualization of complex repeats

Motivation

A common method for analyzing genomic repeats is to produce a sequence similarity matrix visualized via a dot plot. Innovative approaches such as StainedGlass have improved upon this classic visualization by rendering dot plots as a heatmap of sequence identity, enabling researchers to better visualize multi-megabase tandem repeat arrays within centromeres and other heterochromatic regions of the genome. However, computing the similarity estimates for heatmaps requires high computational overhead and can suffer from decreasing accuracy.

Results

In this work we introduce ModDotPlot, an interactive and alignment-free dot plot viewer. By approximating average nucleotide identity via a k-mer-based containment index, ModDotPlot produces accurate plots orders of magnitude faster than StainedGlass. We accomplish this through the use of a hierarchical modimizer scheme that can visualize the full 128 Mbp genome of Arabidopsis thaliana in under 5 minutes on a laptop. ModDotPlot is bundled with a graphical user interface supporting real-time interactive navigation of entire chromosomes.

Availability and Implementation

ModDotPlot is available at https://github.com/marbl/ModDotPlot.

Creating and Using Minimizer Sketches in Computational Genomics

Processing large data sets has become an essential part of computational genomics. Greatly increased availability of sequence data from multiple sources has fueled breakthroughs in genomics and related fields but has led to computational challenges processing large sequencing experiments. The minimizer sketch is a popular method for sequence sketching that underlies core steps in computational genomics such as read mapping, sequence assembling, k-mer counting, and more. In most applications, minimizer sketches are constructed using one of few classical approaches. More recently, efforts have been put into building minimizer sketches with desirable properties compared with the classical constructions. In this survey, we review the history of the minimizer sketch, the theories developed around the concept, and the plethora of applications taking advantage of such sketches. We aim to provide the readers a comprehensive picture of the research landscape involving minimizer sketches, in anticipation of better fusion of theory and application in the future.

LexicHash: sequence similarity estimation via lexicographic comparison of hashes

MOTIVATION

Pairwise sequence alignment is a heavy computational burden, particularly in the context of third-generation sequencing technologies. This issue is commonly addressed by approximately estimating sequence similarities using a hash-based method such as MinHash. In MinHash, all k-mers in a read are hashed and the minimum hash value, the min-hash, is stored. Pairwise similarities can then be estimated by counting the number of min-hash matches between a pair of reads, across many distinct hash functions. The choice of the parameter k controls an important tradeoff in the task of identifying alignments: larger k-values give greater confidence in the identification of alignments (high precision) but can lead to many missing alignments (low recall), particularly in the presence of significant noise.

RESULTS

In this work, we introduce LexicHash, a new similarity estimation method that is effectively independent of the choice of k and attains the high precision of large-k and the high sensitivity of small-k MinHash. LexicHash is a variant of MinHash with a carefully designed hash function. When estimating the similarity between two reads, instead of simply checking whether min-hashes match (as in standard MinHash), one checks how "lexicographically similar" the LexicHash min-hashes are. In our experiments on 40 PacBio datasets, the area under the precision-recall curves obtained by LexicHash had an average improvement of 20.9% over MinHash. Additionally, the LexicHash framework lends itself naturally to an efficient search of the largest alignments, yielding an O(n) time algorithm, and circumventing the seemingly fundamental O(n2) scaling associated with pairwise similarity search.

AVAILABILITY AND IMPLEMENTATION

LexicHash is available on GitHub at https://github.com/gcgreenberg/LexicHash.

Benchmarking assembly free nanopore read mappers to classify complex millipede gut microbiota via Oxford Nanopore Sequencing Technology

Millipedes are key players in recycling leaf litter into soil in tropical ecosystems. To elucidate their gut microbiota, we collected millipedes from different municipalities of Puerto Rico. Here we aim to benchmark which method is best for metagenomic skimming of this highly complex millipede microbiome. We sequenced the gut DNA with Oxford Nanopore Technologies' (ONT) MinION sequencer, then analyzed the data using MEGAN-LR, Kraken2 protein mode, Kraken2 nucleotide mode, GraphMap, and Minimap2 to classify these long ONT reads. From our two samples, we obtained a total of 87,110 and 99,749 ONT reads, respectively. Kraken2 nucleotide mode classified the most reads compared to all other methods at the phylum and class taxonomic level, classifying 75% of the reads in the two samples, the other methods failed to assign enough reads to either phylum or class to yield asymptotes in the taxa rarefaction curves indicating that they required more sequencing depth to fully classify this community. The community is hyper diverse with all methods classifying 20-50 phyla in the two samples. There was significant overlap in the reads used and phyla classified between the five methods benchmarked. Our results suggest that Kraken2 nucleotide mode is the most appropriate tool for the application of metagenomic skimming of this highly complex community.

Efficient mapping of accurate long reads in minimizer space with mapquik

DNA sequencing data continue to progress toward longer reads with increasingly lower sequencing error rates. We focus on the critical problem of mapping, or aligning, low-divergence sequences from long reads (e.g., Pacific Biosciences [PacBio] HiFi) to a reference genome, which poses challenges in terms of accuracy and computational resources when using cutting-edge read mapping approaches that are designed for all types of alignments. A natural idea would be to optimize efficiency with longer seeds to reduce the probability of extraneous matches; however, contiguous exact seeds quickly reach a sensitivity limit. We introduce mapquik, a novel strategy that creates accurate longer seeds by anchoring alignments through matches of k consecutively sampled minimizers (k-min-mers) and only indexing k-min-mers that occur once in the reference genome, thereby unlocking ultrafast mapping while retaining high sensitivity. We show that mapquik significantly accelerates the seeding and chaining steps-fundamental bottlenecks to read mapping-for both the human and maize genomes with [Formula: see text] sensitivity and near-perfect specificity. On the human genome, for both real and simulated reads, mapquik achieves a [Formula: see text] speedup over the state-of-the-art tool minimap2, and on the maize genome, mapquik achieves a [Formula: see text] speedup over minimap2, making mapquik the fastest mapper to date. These accelerations are enabled from not only minimizer-space seeding but also a novel heuristic [Formula: see text] pseudochaining algorithm, which improves upon the long-standing [Formula: see text] bound. Minimizer-space computation builds the foundation for achieving real-time analysis of long-read sequencing data.

JBrowse 2: a modular genome browser with views of synteny and structural variation

We present JBrowse 2, a general-purpose genome annotation browser offering enhanced visualization of complex structural variation and evolutionary relationships. It retains core features of JBrowse while adding new views for synteny, dotplots, breakpoints, gene fusions, and whole-genome overviews. It allows users to share sessions, open multiple genomes, and navigate between views. It can be embedded in a web page, used as a standalone application, or run from Jupyter notebooks or R sessions. These improvements are enabled by a ground-up redesign using modern web technology. We describe application functionality, use cases, performance benchmarks, and implementation notes for web administrators and developers.

G-SAIP: Graphical Sequence Alignment Through Parallel Programming in the Post-Genomic Era

A common task in bioinformatics is to compare DNA sequences to identify similarities between organisms at the sequence level. An approach to such comparison is the dot-plots, a 2-dimensional graphical representation to analyze DNA or protein alignments. Dot-plots alignment software existed before the sequencing revolution, and now there is an ongoing limitation when dealing with large-size sequences, resulting in very long execution times. High-Performance Computing (HPC) techniques have been successfully used in many applications to reduce computing times, but so far, very few applications for graphical sequence alignment using HPC have been reported. Here, we present G-SAIP (Graphical Sequence Alignment in Parallel), a software capable of spawning multiple distributed processes on CPUs, over a supercomputing infrastructure to speed up the execution time for dot-plot generation up to 1.68× compared with other current fastest tools, improve the efficiency for comparative structural genomic analysis, phylogenetics because the benefits of pairwise alignments for comparison between genomes, repetitive structure identification, and assembly quality checking.

Sketching and sampling approaches for fast and accurate long read classification

BACKGROUND

In modern sequencing experiments, quickly and accurately identifying the sources of the reads is a crucial need. In metagenomics, where each read comes from one of potentially many members of a community, it can be important to identify the exact species the read is from. In other settings, it is important to distinguish which reads are from the targeted sample and which are from potential contaminants. In both cases, identification of the correct source of a read enables further investigation of relevant reads, while minimizing wasted work. This task is particularly challenging for long reads, which can have a substantial error rate that obscures the origins of each read.

RESULTS

Existing tools for the read classification problem are often alignment or index-based, but such methods can have large time and/or space overheads. In this work, we investigate the effectiveness of several sampling and sketching-based approaches for read classification. In these approaches, a chosen sampling or sketching algorithm is used to generate a reduced representation (a "screen") of potential source genomes for a query readset before reads are streamed in and compared against this screen. Using a query read's similarity to the elements of the screen, the methods predict the source of the read. Such an approach requires limited pre-processing, stores and works with only a subset of the input data, and is able to perform classification with a high degree of accuracy.

CONCLUSIONS

The sampling and sketching approaches investigated include uniform sampling, methods based on MinHash and its weighted and order variants, a minimizer-based technique, and a novel clustering-based sketching approach. We demonstrate the effectiveness of these techniques both in identifying the source microbial genomes for reads from a metagenomic long read sequencing experiment, and in distinguishing between long reads from organisms of interest and potential contaminant reads. We then compare these approaches to existing alignment, index and sketching-based tools for read classification, and demonstrate how such a method is a viable alternative for determining the source of query reads. Finally, we present a reference implementation of these approaches at https://github.com/arun96/sketching .

Ribosomal MLST nucleotide identity (rMLST-NI), a rapid bacterial species identification method: application to Klebsiella and Raoultella genomic species validation

Bacterial genomics is making an increasing contribution to the fields of medicine and public health microbiology. Consequently, accurate species identification of bacterial genomes is an important task, particularly as the number of genomes stored in online databases increases rapidly and new species are frequently discovered. Existing database entries require regular re-evaluation to ensure that species annotations are consistent with the latest species definitions. We have developed an automated method for bacterial species identification that is an extension of ribosomal multilocus sequence typing (rMLST). The method calculates an ‘rMLST nucleotide identity’ (rMLST-NI) based on the nucleotides present in the protein-encoding ribosomal genes derived from bacterial genomes. rMLST-NI was used to validate the species annotations of 11839 publicly available Klebsiella and Raoultella genomes based on a comparison with a library of type strain genomes. rMLST-NI was compared with two whole-genome average nucleotide identity methods (OrthoANIu and FastANI) and the k-mer based Kleborate software. The results of the four methods agreed across a dataset of 11839 bacterial genomes and identified a small number of entries (n=89) with species annotations that required updating. The rMLST-NI method was 3.5 times faster than Kleborate, 4.5 times faster than FastANI and 1600 times faster than OrthoANIu. rMLST-NI represents a fast and generic method for species identification using type strains as a reference.

Multiple genome alignment in the telomere-to-telomere assembly era

With the arrival of telomere-to-telomere (T2T) assemblies of the human genome comes the computational challenge of efficiently and accurately constructing multiple genome alignments at an unprecedented scale. By identifying nucleotides across genomes which share a common ancestor, multiple genome alignments commonly serve as the bedrock for comparative genomics studies. In this review, we provide an overview of the algorithmic template that most multiple genome alignment methods follow. We also discuss prospective areas of improvement of multiple genome alignment for keeping up with continuously arriving high-quality T2T assembled genomes and for unlocking clinically-relevant insights.

Long-read mapping to repetitive reference sequences using Winnowmap2

Approximately 5-10% of the human genome remains inaccessible due to the presence of repetitive sequences such as segmental duplications and tandem repeat arrays. We show that existing long-read mappers often yield incorrect alignments and variant calls within long, near-identical repeats, as they remain vulnerable to allelic bias. In the presence of a nonreference allele within a repeat, a read sampled from that region could be mapped to an incorrect repeat copy. To address this limitation, we developed a new long-read mapping method, Winnowmap2, by using minimal confidently alignable substrings. Winnowmap2 computes each read mapping through a collection of confident subalignments. This approach is more tolerant of structural variation and more sensitive to paralog-specific variants within repeats. Our experiments highlight that Winnowmap2 successfully addresses the issue of allelic bias, enabling more accurate downstream variant calls in repetitive sequences.

ANI analysis of poxvirus genomes reveals its potential application to viral species rank demarcation

Average nucleotide identity (ANI) is a prominent approach for rapidly classifying archaea and bacteria by recruiting both whole genomic sequences and draft assemblies. To evaluate the feasibility of ANI in virus taxon demarcation, 685 poxviruses were assessed. Prior to the analysis, the fragment length and threshold of the ANI value were optimized as 200 bp and 98 per cent, respectively. After ANI analysis and network visualization, the resulting sixty-one species (ANI species rank) were clustered and largely consistent with the groupings found in National Center for Biotechnology Information Virus [within the International Committee on Taxonomy of Viruses (ICTV) Master Species List]. The species identities of thirty-four other poxviruses (excluded by the ICTV Master Species List) were also identified. Subsequent phylogenetic analysis and Guanine-Cytosine (GC) content comparison done were found to support the ANI analysis. Finally, the BLAST identity of concatenated sequences from previously identified core genes showed 91.8 per cent congruence with ANI analysis at the species rank, thus showing potential as a marker gene for poxviruses classification. Collectively, our results reveal that the ANI analysis may serve as a novel and efficient method for poxviruses demarcation.

Effective sequence similarity detection with strobemers

k-mer-based methods are widely used in bioinformatics for various types of sequence comparisons. However, a single mutation will mutate k consecutive k-mers and make most k-mer-based applications for sequence comparison sensitive to variable mutation rates. Many techniques have been studied to overcome this sensitivity, for example, spaced k-mers and k-mer permutation techniques, but these techniques do not handle indels well. For indels, pairs or groups of small k-mers are commonly used, but these methods first produce k-mer matches, and only in a second step, a pairing or grouping of k-mers is performed. Such techniques produce many redundant k-mer matches owing to the size of k Here, we propose strobemers as an alternative to k-mers for sequence comparison. Intuitively, strobemers consist of two or more linked shorter k-mers, where the combination of linked k-mers is decided by a hash function. We use simulated data to show that strobemers provide more evenly distributed sequence matches and are less sensitive to different mutation rates than k-mers and spaced k-mers. Strobemers also produce higher match coverage across sequences. We further implement a proof-of-concept sequence-matching tool StrobeMap and use synthetic and biological Oxford Nanopore sequencing data to show the utility of using strobemers for sequence comparison in different contexts such as sequence clustering and alignment scenarios.

Vulcan: Improved long-read mapping and structural variant calling via dual-mode alignment

BACKGROUND

Long-read sequencing has enabled unprecedented surveys of structural variation across the entire human genome. To maximize the potential of long-read sequencing in this context, novel mapping methods have emerged that have primarily focused on either speed or accuracy. Various heuristics and scoring schemas have been implemented in widely used read mappers (minimap2 and NGMLR) to optimize for speed or accuracy, which have variable performance across different genomic regions and for specific structural variants. Our hypothesis is that constraining read mapping to the use of a single gap penalty across distinct mutational hot spots reduces read alignment accuracy and impedes structural variant detection.

FINDINGS

We tested our hypothesis by implementing a read-mapping pipeline called Vulcan that uses two distinct gap penalty modes, which we refer to as dual-mode alignment. The high-level idea is that Vulcan leverages the computed normalized edit distance of the mapped reads via minimap2 to identify poorly aligned reads and realigns them using the more accurate yet computationally more expensive long-read mapper (NGMLR). In support of our hypothesis, we show that Vulcan improves the alignments for Oxford Nanopore Technology long reads for both simulated and real datasets. These improvements, in turn, lead to improved accuracy for structural variant calling performance on human genome datasets compared to either of the read-mapping methods alone.

CONCLUSIONS

Vulcan is the first long-read mapping framework that combines two distinct gap penalty modes for improved structural variant recall and precision. Vulcan is open-source and available under the MIT License at https://gitlab.com/treangenlab/vulcan.

Technology dictates algorithms: recent developments in read alignment

Aligning sequencing reads onto a reference is an essential step of the majority of genomic analysis pipelines. Computational algorithms for read alignment have evolved in accordance with technological advances, leading to today's diverse array of alignment methods. We provide a systematic survey of algorithmic foundations and methodologies across 107 alignment methods, for both short and long reads. We provide a rigorous experimental evaluation of 11 read aligners to demonstrate the effect of these underlying algorithms on speed and efficiency of read alignment. We discuss how general alignment algorithms have been tailored to the specific needs of various domains in biology.

The microbiome and host mucosal interactions in urinary tract diseases

The urinary tract consists of the bladder, ureters, and kidneys, and is an essential organ system for filtration and excretion of waste products and maintaining systemic homeostasis. In this capacity, the urinary tract is impacted by its interactions with other mucosal sites, including the genitourinary and gastrointestinal systems. Each of these sites harbors diverse ecosystems of microbes termed the microbiota, that regulates complex interactions with the local and systemic immune system. It remains unclear whether changes in the microbiota and associated metabolites may be a consequence or a driver of urinary tract diseases. Here, we review the current literature, investigating the impact of the microbiota on the urinary tract in homeostasis and disease including urinary stones, acute kidney injury, chronic kidney disease, and urinary tract infection. We propose new avenues for exploration of the urinary microbiome using emerging technology and discuss the potential of microbiome-based medicine for urinary tract conditions.

Puffaligner : A Fast, Efficient, and Accurate Aligner Based on the Pufferfish Index

Motivation

Sequence alignment is one of the first steps in many modern genomic analyses, such as variant detection, transcript abundance estimation and metagenomic profiling. Unfortunately, it is often a computationally expensive procedure. As the quantity of data and wealth of different assays and applications continue to grow, the need for accurate and fast alignment tools that scale to large collections of reference sequences persists.

Results

In this article, we introduce PuffAligner, a fast, accurate and versatile aligner built on top of the Pufferfish index. PuffAligner is able to produce highly sensitive alignments, similar to those of Bowtie2, but much more quickly. While exhibiting similar speed to the ultrafast STAR aligner, PuffAligner requires considerably less memory to construct its index and align reads. PuffAligner strikes a desirable balance with respect to the time, space and accuracy tradeoffs made by different alignment tools and provides a promising foundation on which to test new alignment ideas over large collections of sequences.

Availability and implementation

All the data used for preparing the results of this paper can be found with 10.5281/zenodo.4902332. PuffAligner is a free and open-source software. It is implemented in C++14 and can be obtained from https://github.com/COMBINE-lab/pufferfish/tree/cigar-strings.

Supplementary information

Supplementary data are available at Bioinformatics online.

LINflow: a computational pipeline that combines an alignment-free with an alignment-based method to accelerate generation of similarity matrices for prokaryotic genomes

Background

Computing genomic similarity between strains is a prerequisite for genome-based prokaryotic classification and identification. Genomic similarity was first computed as Average Nucleotide Identity (ANI) values based on the alignment of genomic fragments. Since this is computationally expensive, faster and computationally cheaper alignment-free methods have been developed to estimate ANI. However, these methods do not reach the level of accuracy of alignment-based methods.

Methods

Here we introduce LINflow, a computational pipeline that infers pairwise genomic similarity in a set of genomes. LINflow takes advantage of the speed of the alignment-free sourmash tool to identify the genome in a dataset that is most similar to a query genome and the precision of the alignment-based pyani software to precisely compute ANI between the query genome and the most similar genome identified by sourmash. This is repeated for each new genome that is added to a dataset. The sequentially computed ANI values are stored as Life Identification Numbers (LINs), which are then used to infer all other pairwise ANI values in the set. We tested LINflow on four sets, 484 genomes in total, and compared the needed time and the generated similarity matrices with other tools.

Results

LINflow is up to 150 times faster than pyani and pairwise ANI values generated by LINflow are highly correlated with those computed by pyani. However, because LINflow infers most pairwise ANI values instead of computing them directly, ANI values occasionally depart from the ANI values computed by pyani. In conclusion, LINflow is a fast and memory-efficient pipeline to infer similarity among a large set of prokaryotic genomes. Its ability to quickly add new genome sequences to an already computed similarity matrix makes LINflow particularly useful for projects when new genome sequences need to be regularly added to an existing dataset.

BugSeq: a highly accurate cloud platform for long-read metagenomic analyses

Background

As the use of nanopore sequencing for metagenomic analysis increases, tools capable of performing long-read taxonomic classification (ie. determining the composition of a sample) in a fast and accurate manner are needed. Existing tools were either designed for short-read data (eg. Centrifuge), take days to analyse modern sequencer outputs (eg. MetaMaps) or suffer from suboptimal accuracy (eg. CDKAM). Additionally, all tools require command line expertise and do not scale in the cloud.

Results

We present BugSeq, a novel, highly accurate metagenomic classifier for nanopore reads. We evaluate BugSeq on simulated data, mock microbial communities and real clinical samples. On the ZymoBIOMICS Even and Log communities, BugSeq (F1 = 0.95 at species level) offers better read classification than MetaMaps (F1 = 0.89–0.94) in a fraction of the time. BugSeq significantly improves on the accuracy of Centrifuge (F1 = 0.79–0.93) and CDKAM (F1 = 0.91–0.94) while offering competitive run times. When applied to 41 samples from patients with lower respiratory tract infections, BugSeq produces greater concordance with microbiological culture and qPCR compared with “What’s In My Pot” analysis.

Conclusion

BugSeq is deployed to the cloud for easy and scalable long-read metagenomic analyses. BugSeq is freely available for non-commercial use at https://bugseq.com/free.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04089-5.

On the transformation of MinHash-based uncorrected distances into proper evolutionary distances for phylogenetic inference

Recently developed MinHash-based techniques were proven successful in quickly estimating the level of similarity between large nucleotide sequences. This article discusses their usage and limitations in practice to approximating uncorrected distances between genomes, and transforming these pairwise dissimilarities into proper evolutionary distances. It is notably shown that complex distance measures can be easily approximated using simple transformation formulae based on few parameters. MinHash-based techniques can therefore be very useful for implementing fast yet accurate alignment-free phylogenetic reconstruction procedures from large sets of genomes. This last point of view is assessed with a simulation study using a dedicated bioinformatics tool.

TandemTools: mapping long reads and assessing/improving assembly quality in extra-long tandem repeats

MOTIVATION

Extra-long tandem repeats (ETRs) are widespread in eukaryotic genomes and play an important role in fundamental cellular processes, such as chromosome segregation. Although emerging long-read technologies have enabled ETR assemblies, the accuracy of such assemblies is difficult to evaluate since there are no tools for their quality assessment. Moreover, since the mapping of error-prone reads to ETRs remains an open problem, it is not clear how to polish draft ETR assemblies.

RESULTS

To address these problems, we developed the TandemTools software that includes the TandemMapper tool for mapping reads to ETRs and the TandemQUAST tool for polishing ETR assemblies and their quality assessment. We demonstrate that TandemTools not only reveals errors in ETR assemblies but also improves the recently generated assemblies of human centromeres.

AVAILABILITY AND IMPLEMENTATION

https://github.com/ablab/TandemTools.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Weighted minimizer sampling improves long read mapping

MOTIVATION

In this era of exponential data growth, minimizer sampling has become a standard algorithmic technique for rapid genome sequence comparison. This technique yields a sub-linear representation of sequences, enabling their comparison in reduced space and time. A key property of the minimizer technique is that if two sequences share a substring of a specified length, then they can be guaranteed to have a matching minimizer. However, because the k-mer distribution in eukaryotic genomes is highly uneven, minimizer-based tools (e.g. Minimap2, Mashmap) opt to discard the most frequently occurring minimizers from the genome to avoid excessive false positives. By doing so, the underlying guarantee is lost and accuracy is reduced in repetitive genomic regions.

RESULTS

We introduce a novel weighted-minimizer sampling algorithm. A unique feature of the proposed algorithm is that it performs minimizer sampling while considering a weight for each k-mer; i.e. the higher the weight of a k-mer, the more likely it is to be selected. By down-weighting frequently occurring k-mers, we are able to meet both objectives: (i) avoid excessive false-positive matches and (ii) maintain the minimizer match guarantee. We tested our algorithm, Winnowmap, using both simulated and real long-read data and compared it to a state-of-the-art long read mapper, Minimap2. Our results demonstrate a reduction in the mapping error-rate from 0.14% to 0.06% in the recently finished human X chromosome (154.3 Mbp), and from 3.6% to 0% within the highly repetitive X centromere (3.1 Mbp). Winnowmap improves mapping accuracy within repeats and achieves these results with sparser sampling, leading to better index compression and competitive runtimes.

AVAILABILITY AND IMPLEMENTATION

Winnowmap is built on top of the Minimap2 codebase and is available at https://github.com/marbl/winnowmap.

Trans-NanoSim characterizes and simulates nanopore RNA-sequencing data

Background

Compared with second-generation sequencing technologies, third-generation single-molecule RNA sequencing has unprecedented advantages; the long reads it generates facilitate isoform-level transcript characterization. In particular, the Oxford Nanopore Technology sequencing platforms have become more popular in recent years owing to their relatively high affordability and portability compared with other third-generation sequencing technologies. To aid the development of analytical tools that leverage the power of this technology, simulated data provide a cost-effective solution with ground truth. However, a nanopore sequence simulator targeting transcriptomic data is not available yet.

Findings

We introduce Trans-NanoSim, a tool that simulates reads with technical and transcriptome-specific features learnt from nanopore RNA-sequncing data. We comprehensively benchmarked Trans-NanoSim on direct RNA and complementary DNA datasets describing human and mouse transcriptomes. Through comparison against other nanopore read simulators, we show the unique advantage and robustness of Trans-NanoSim in capturing the characteristics of nanopore complementary DNA and direct RNA reads.

Conclusions

As a cost-effective alternative to sequencing real transcriptomes, Trans-NanoSim will facilitate the rapid development of analytical tools for nanopore RNA-sequencing data. Trans-NanoSim and its pre-trained models are freely accessible at https://github.com/bcgsc/NanoSim.

To Petabytes and beyond: recent advances in probabilistic and signal processing algorithms and their application to metagenomics

As computational biologists continue to be inundated by ever increasing amounts of metagenomic data, the need for data analysis approaches that keep up with the pace of sequence archives has remained a challenge. In recent years, the accelerated pace of genomic data availability has been accompanied by the application of a wide array of highly efficient approaches from other fields to the field of metagenomics. For instance, sketching algorithms such as MinHash have seen a rapid and widespread adoption. These techniques handle increasingly large datasets with minimal sacrifices in quality for tasks such as sequence similarity calculations. Here, we briefly review the fundamentals of the most impactful probabilistic and signal processing algorithms. We also highlight more recent advances to augment previous reviews in these areas that have taken a broader approach. We then explore the application of these techniques to metagenomics, discuss their pros and cons, and speculate on their future directions.

Continuous chromosome-scale haplotypes assembled from a single interspecies F1 hybrid of yak and cattle

BACKGROUND

The development of trio binning as an approach for assembling diploid genomes has enabled the creation of fully haplotype-resolved reference genomes. Unlike other methods of assembly for diploid genomes, this approach is enhanced, rather than hindered, by the heterozygosity of the individual sequenced. To maximize heterozygosity and simultaneously assemble reference genomes for 2 species, we applied trio binning to an interspecies F1 hybrid of yak (Bos grunniens) and cattle (Bos taurus), 2 species that diverged nearly 5 million years ago. The genomes of both of these species are composed of acrocentric autosomes.

RESULTS

We produced the most continuous haplotype-resolved assemblies for a diploid animal yet reported. Both the maternal (yak) and paternal (cattle) assemblies have the largest 2 chromosomes in single haplotigs, and more than one-third of the autosomes similarly lack gaps. The maximum length haplotig produced was 153 Mb without any scaffolding or gap-filling steps and represents the longest haplotig reported for any species. The assemblies are also more complete and accurate than those reported for most other vertebrates, with 97% of mammalian universal single-copy orthologs present.

CONCLUSIONS

The high heterozygosity inherent to interspecies crosses maximizes the effectiveness of the trio binning method. The interspecies trio binning approach we describe is likely to provide the highest-quality assemblies for any pair of species that can interbreed to produce hybrid offspring that develop to sufficient cell numbers for DNA extraction.

When the levee breaks: a practical guide to sketching algorithms for processing the flood of genomic data

Considerable advances in genomics over the past decade have resulted in vast amounts of data being generated and deposited in global archives. The growth of these archives exceeds our ability to process their content, leading to significant analysis bottlenecks. Sketching algorithms produce small, approximate summaries of data and have shown great utility in tackling this flood of genomic data, while using minimal compute resources. This article reviews the current state of the field, focusing on how the algorithms work and how genomicists can utilize them effectively. References to interactive workbooks for explaining concepts and demonstrating workflows are included at https://github.com/will-rowe/genome-sketching .

Strain-level metagenomic assignment and compositional estimation for long reads with MetaMaps

Metagenomic sequence classification should be fast, accurate and information-rich. Emerging long-read sequencing technologies promise to improve the balance between these factors but most existing methods were designed for short reads. MetaMaps is a new method, specifically developed for long reads, capable of mapping a long-read metagenome to a comprehensive RefSeq database with >12,000 genomes in <16 GB or RAM on a laptop computer. Integrating approximate mapping with probabilistic scoring and EM-based estimation of sample composition, MetaMaps achieves >94% accuracy for species-level read assignment and r² > 0.97 for the estimation of sample composition on both simulated and real data when the sample genomes or close relatives are present in the classification database. To address novel species and genera, which are comparatively harder to predict, MetaMaps outputs mapping locations and qualities for all classified reads, enabling functional studies (e.g. gene presence/absence) and detection of incongruities between sample and reference genomes.

Sequencing platforms, such as Oxford Nanopore or Pacific Biosciences generate long-read data that preserve long-range genomic information but have high error rates. Here, the authors develop MetaMaps, a computational tool for strain-level metagenomic assignment and compositional estimation using long reads.

Whole-Genome Alignment and Comparative Annotation

Rapidly improving sequencing technology coupled with computational developments in sequence assembly are making reference-quality genome assembly economical. Hundreds of vertebrate genome assemblies are now publicly available, and projects are being proposed to sequence thousands of additional species in the next few years. Such dense sampling of the tree of life should give an unprecedented new understanding of evolution and allow a detailed determination of the events that led to the wealth of biodiversity around us. To gain this knowledge, these new genomes must be compared through genome alignment (at the sequence level) and comparative annotation (at the gene level). However, different alignment and annotation methods have different characteristics; before starting a comparative genomics analysis, it is important to understand the nature of, and biases and limitations inherent in, the chosen methods. This review is intended to act as a technical but high-level overview of the field that should provide this understanding. We briefly survey the state of the genome alignment and comparative annotation fields and potential future directions for these fields in a new, large-scale era of comparative genomics.