Tackling reference bias in genotyping by using founder sequences with PanVC 3.

Summary: Overcoming reference bias and calling insertions and deletions are major challenges in genotyping. We present PanVC 3, a set of software that can be utilized as part of various variant calling workflows. We show that, by incorporating known genetic variants to a set of founder sequences to which reads are aligned, reference bias is reduced and precision of calling insertions and deletions is improved. Availability and implementation: PanVC 3 and its source code are freely available at https://github.com/tsnorri/panvc3 and at https://anaconda.org/tsnorri/panvc3 under the MIT licence. The experiment scripts are available at https://github.com/algbio/panvc3-experiments.

Finding maximal exact matches in graphs.

BACKGROUND: We study the problem of finding maximal exact matches (MEMs) between a query string Q and a labeled graph G. MEMs are an important class of seeds, often used in seed-chain-extend type of practical alignment methods because of their strong connections to classical metrics. A principled way to speed up chaining is to limit the number of MEMs by considering only MEMs of length at least κ ( κ -MEMs). However, on arbitrary input graphs, the problem of finding MEMs cannot be solved in truly sub-quadratic time under SETH (Equi et al., TALG 2023) even on acyclic graphs. RESULTS: In this paper we show an O ( n · L · d L - 1 + m + M κ , L ) -time algorithm finding all κ -MEMs between Q and G spanning exactly L nodes in G, where n is the total length of node labels, d is the maximum degree of a node in G, m = | Q | , and M κ , L is the number of output MEMs. We use this algorithm to develop a κ -MEM finding solution on indexable Elastic Founder Graphs (Equi et al., Algorithmica 2022) running in time O ( n H 2 + m + M κ ) , where H is the maximum number of nodes in a block, and M κ is the total number of κ -MEMs. Our results generalize to the analysis of multiple query strings (MEMs between G and any of the strings). Additionally, we provide some experimental results showing that the number of graph MEMs is an order of magnitude smaller than the number of string MEMs of the corresponding concatenated collection. CONCLUSIONS: We show that seed-chain-extend type of alignment methods can be implemented on top of indexable Elastic Founder Graphs by providing an efficient way to produce the seeds between a set of queries and the graph. The code is available in https://github.com/algbio/efg-mems .

Chaining for accurate alignment of erroneous long reads to acyclic variation graphs.

MOTIVATION: Aligning reads to a variation graph is a standard task in pangenomics, with downstream applications such as improving variant calling. While the vg toolkit [Garrison et al. (Variation graph toolkit improves read mapping by representing genetic variation in the reference. Nat Biotechnol 2018;36:875-9)] is a popular aligner of short reads, GraphAligner [Rautiainen and Marschall (GraphAligner: rapid and versatile sequence-to-graph alignment. Genome Biol 2020;21:253-28)] is the state-of-the-art aligner of erroneous long reads. GraphAligner works by finding candidate read occurrences based on individually extending the best seeds of the read in the variation graph. However, a more principled approach recognized in the community is to co-linearly chain multiple seeds. RESULTS: We present a new algorithm to co-linearly chain a set of seeds in a string labeled acyclic graph, together with the first efficient implementation of such a co-linear chaining algorithm into a new aligner of erroneous long reads to acyclic variation graphs, GraphChainer. We run experiments aligning real and simulated PacBio CLR reads with average error rates 15% and 5%. Compared to GraphAligner, GraphChainer aligns 12-17% more reads, and 21-28% more total read length, on real PacBio CLR reads from human chromosomes 1, 22, and the whole human pangenome. On both simulated and real data, GraphChainer aligns between 95% and 99% of all reads, and of total read length. We also show that minigraph [Li et al. (The design and construction of reference pangenome graphs with minigraph. Genome Biol 2020;21:265-19.)] and minichain [Chandra and Jain (Sequence to graph alignment using gap-sensitive co-linear chaining. In: Proceedings of the 27th Annual International Conference on Research in Computational Molecular Biology (RECOMB 2023). Springer, 2023, 58-73.)] obtain an accuracy of <60% on this setting. AVAILABILITY AND IMPLEMENTATION: GraphChainer is freely available at https://github.com/algbio/GraphChainer. The datasets and evaluation pipeline can be reached from the previous address.

Bacterial genomic epidemiology with mixed samples.

Genomic epidemiology is a tool for tracing transmission of pathogens based on whole-genome sequencing. We introduce the mGEMS pipeline for genomic epidemiology with plate sweeps representing mixed samples of a target pathogen, opening the possibility to sequence all colonies on selective plates with a single DNA extraction and sequencing step. The pipeline includes the novel mGEMS read binner for probabilistic assignments of sequencing reads, and the scalable pseudoaligner Themisto. We demonstrate the effectiveness of our approach using closely related samples in a nosocomial setting, obtaining results that are comparable to those based on single-colony picks. Our results lend firm support to more widespread consideration of genomic epidemiology with mixed infection samples.

Distributed hybrid-indexing of compressed pan-genomes for scalable and fast sequence alignment.

Computational pan-genomics utilizes information from multiple individual genomes in large-scale comparative analysis. Genetic variation between case-controls, ethnic groups, or species can be discovered thoroughly using pan-genomes of such subpopulations. Whole-genome sequencing (WGS) data volumes are growing rapidly, making genomic data compression and indexing methods very important. Despite current space-efficient repetitive sequence compression and indexing methods, the deployed compression methods are often sequential, computationally time-consuming, and do not provide efficient sequence alignment performance on vast collections of genomes such as pan-genomes. For performing rapid analytics with the ever-growing genomics data, data compression and indexing methods have to exploit distributed and parallel computing more efficiently. Instead of strict genome data compression methods, we will focus on the efficient construction of a compressed index for pan-genomes. Compressed hybrid-index enables fast sequence alignments to several genomes at once while shrinking the index size significantly compared to traditional indexes. We propose a scalable distributed compressed hybrid-indexing method for large genomic data sets enabling pan-genome-based sequence search and read alignment capabilities. We show the scalability of our tool, DHPGIndex, by executing experiments in a distributed Apache Spark-based computing cluster comprising 448 cores distributed over 26 nodes. The experiments have been performed both with human and bacterial genomes. DHPGIndex built a BLAST index for n = 250 human pan-genome with an 870:1 compression ratio (CR) in 342 minutes and a Bowtie2 index with 157:1 CR in 397 minutes. For n = 1,000 human pan-genome, the BLAST index was built in 1520 minutes with 532:1 CR and the Bowtie2 index in 1938 minutes with 76:1 CR. Bowtie2 aligned 14.6 GB of paired-end reads to the compressed (n = 1,000) index in 31.7 minutes on a single node. Compressing n = 13,375,031 (488 GB) GenBank database to BLAST index resulted in CR of 62:1 in 575 minutes. BLASTing 189,864 Crispr-Cas9 gRNA target sequences (23 MB in total) to the compressed index of human pan-genome (n = 1,000) finished in 45 minutes on a single node. 30 MB mixed bacterial sequences were (n = 599) were blasted to the compressed index of 488 GB GenBank database (n = 13,375,031) in 26 minutes on 25 nodes. 78 MB mixed sequences (n = 4,167) were blasted to the compressed index of 18 GB E. coli sequence database (n = 745,409) in 5.4 minutes on a single node.

Founder reconstruction enables scalable and seamless pangenomic analysis.

MOTIVATION: Variant calling workflows that utilize a single reference sequence are the de facto standard elementary genomic analysis routine for resequencing projects. Various ways to enhance the reference with pangenomic information have been proposed, but scalability combined with seamless integration to existing workflows remains a challenge. RESULTS: We present PanVC with founder sequences, a scalable and accurate variant calling workflow based on a multiple alignment of reference sequences. Scalability is achieved by removing duplicate parts up to a limit into a founder multiple alignment, that is then indexed using a hybrid scheme that exploits general purpose read aligners. Our implemented workflow uses GATK or BCFtools for variant calling, but the various steps of our workflow (e.g. vcf2multialign tool, founder reconstruction) can be of independent interest as a basis for creating novel pangenome analysis workflows beyond variant calling. AVAILABILITY AND IMPLEMENTATION: Our open access tools and instructions how to reproduce our experiments are available at the following address: https://github.com/algbio/panvc-founders. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Accurate spliced alignment of long RNA sequencing reads.

MOTIVATION: Long-read RNA sequencing technologies are establishing themselves as the primary techniques to detect novel isoforms, and many such analyses are dependent on read alignments. However, the error rate and sequencing length of the reads create new challenges for accurately aligning them, particularly around small exons. RESULTS: We present an alignment method uLTRA for long RNA sequencing reads based on a novel two-pass collinear chaining algorithm. We show that uLTRA produces higher accuracy over state-of-the-art aligners with substantially higher accuracy for small exons on simulated and synthetic data. On simulated data, uLTRA achieves an accuracy of about 60% for exons of length 10 nucleotides or smaller and close to 90% accuracy for exons of length between 11 and 20 nucleotides. On biological data where true read location is unknown, we show several examples where uLTRA aligns to known and novel isoforms containing small exons that are not detected with other aligners. While uLTRA obtains its accuracy using annotations, it can also be used as a wrapper around minimap2 to align reads outside annotated regions. AVAILABILITYAND IMPLEMENTATION: uLTRA is available at https://github.com/ksahlin/ultra. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Linear time minimum segmentation enables scalable founder reconstruction.

BACKGROUND:  We study a preprocessing routine relevant in pan-genomic analyses: consider a set of aligned haplotype sequences of complete human chromosomes. Due to the enormous size of such data, one would like to represent this input set with a few founder sequences that retain as well as possible the contiguities of the original sequences. Such a smaller set gives a scalable way to exploit pan-genomic information in further analyses (e.g. read alignment and variant calling). Optimizing the founder set is an NP-hard problem, but there is a segmentation formulation that can be solved in polynomial time, defined as follows. Given a threshold L and a set R = { R 1 , … , R m } of m strings (haplotype sequences), each having length n, the minimum segmentation problem for founder reconstruction is to partition [1, n] into set P of disjoint segments such that each segment [ a , b ] ∈ P has length at least L and the number d ( a , b ) = | { R i [ a , b ] : 1 ≤ i ≤ m } | of distinct substrings at segment [a, b] is minimized over [ a , b ] ∈ P . The distinct substrings in the segments represent founder blocks that can be concatenated to form max { d ( a , b ) : [ a , b ] ∈ P } founder sequences representing the original R such that crossovers happen only at segment boundaries. RESULTS:  We give an O(mn) time (i.e. linear time in the input size) algorithm to solve the minimum segmentation problem for founder reconstruction, improving over an earlier O ( m n 2 ) . CONCLUSIONS:  Our improvement enables to apply the formulation on an input of thousands of complete human chromosomes. We implemented the new algorithm and give experimental evidence on its practicality. The implementation is available in https://github.com/tsnorri/founder-sequences.

Bit-parallel sequence-to-graph alignment.

MOTIVATION: Graphs are commonly used to represent sets of sequences. Either edges or nodes can be labeled by sequences, so that each path in the graph spells a concatenated sequence. Examples include graphs to represent genome assemblies, such as string graphs and de Bruijn graphs, and graphs to represent a pan-genome and hence the genetic variation present in a population. Being able to align sequencing reads to such graphs is a key step for many analyses and its applications include genome assembly, read error correction and variant calling with respect to a variation graph. RESULTS: We generalize two linear sequence-to-sequence algorithms to graphs: the Shift-And algorithm for exact matching and Myers' bitvector algorithm for semi-global alignment. These linear algorithms are both based on processing w sequence characters with a constant number of operations, where w is the word size of the machine (commonly 64), and achieve a speedup of up to w over naive algorithms. For a graph with |V| nodes and |E| edges and a sequence of length m, our bitvector-based graph alignment algorithm reaches a worst case runtime of O(|V|+⌈mw⌉|E| log w) for acyclic graphs and O(|V|+m|E| log w) for arbitrary cyclic graphs. We apply it to five different types of graphs and observe a speedup between 3-fold and 20-fold compared with a previous (asymptotically optimal) alignment algorithm. AVAILABILITY AND IMPLEMENTATION: https://github.com/maickrau/GraphAligner. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

MIPUP: minimum perfect unmixed phylogenies for multi-sampled tumors via branchings and ILP.

MOTIVATION: Discovering the evolution of a tumor may help identify driver mutations and provide a more comprehensive view on the history of the tumor. Recent studies have tackled this problem using multiple samples sequenced from a tumor, and due to clinical implications, this has attracted great interest. However, such samples usually mix several distinct tumor subclones, which confounds the discovery of the tumor phylogeny. RESULTS: We study a natural problem formulation requiring to decompose the tumor samples into several subclones with the objective of forming a minimum perfect phylogeny. We propose an Integer Linear Programming formulation for it, and implement it into a method called MIPUP. We tested the ability of MIPUP and of four popular tools LICHeE, AncesTree, CITUP, Treeomics to reconstruct the tumor phylogeny. On simulated data, MIPUP shows up to a 34% improvement under the ancestor-descendant relations metric. On four real datasets, MIPUP's reconstructions proved to be generally more faithful than those of LICHeE. AVAILABILITY AND IMPLEMENTATION: MIPUP is available at https://github.com/zhero9/MIPUP as open source. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Hardness of Covering Alignment: Phase Transition in Post-Sequence Genomics.

Covering alignment problems arise from recent developments in genomics; so called pan-genome graphs are replacing reference genomes, and advances in haplotyping enable full content of diploid genomes to be used as basis of sequence analysis. In this paper, we show that the computational complexity will change for natural extensions of alignments to pan-genome representations and to diploid genomes. More broadly, our approach can also be seen as a minimal extension of sequence alignment to labelled directed acyclic graphs (labeled DAGs). Namely, we show that finding a covering alignment of two labeled DAGs is NP-hard even on binary alphabets. A covering alignment asks for two paths R1 (red) and G1 (green) in DAG D1 and two paths R2 (red) and G2 (green) in DAG D2 that cover the nodes of the graphs and maximize the sum of the global alignment scores: as(sp(R1),sp(R2))+as(sp(G1),sp(G2)), where sp(P) is the concatenation of labels on the path P. Pair-wise alignment of haplotype sequences forming a diploid chromosome can be converted to a two-path coverable labelled DAG, and then the covering alignment models the similarity of two diploids over arbitrary recombinations. We also give a reduction to the other direction, to show that such a recombination-oblivious diploid alignment is NP-hard on alphabets of size 3.

Discovery of potential causative mutations in human coding and noncoding genome with the interactive software BasePlayer.

Next-generation sequencing (NGS) is routinely applied in life sciences and clinical practice, but interpretation of the massive quantities of genomic data produced has become a critical challenge. The genome-wide mutation analyses enabled by NGS have had a revolutionary impact in revealing the predisposing and driving DNA alterations behind a multitude of disorders. The workflow to identify causative mutations from NGS data, for example in cancer and rare diseases, commonly involves phases such as quality filtering, case-control comparison, genome annotation, and visual validation, which require multiple processing steps and usage of various tools and scripts. To this end, we have introduced an interactive and user-friendly multi-platform-compatible software, BasePlayer, which allows scientists, regardless of bioinformatics training, to carry out variant analysis in disease genetics settings. A genome-wide scan of regulatory regions for mutation clusters can be carried out with a desktop computer in ~10 min with a dataset of 3 million somatic variants in 200 whole-genome-sequenced (WGS) cancers.

Towards pan-genome read alignment to improve variation calling.

BACKGROUND: Typical human genome differs from the reference genome at 4-5 million sites. This diversity is increasingly catalogued in repositories such as ExAC/gnomAD, consisting of >15,000 whole-genomes and >126,000 exome sequences from different individuals. Despite this enormous diversity, resequencing data workflows are still based on a single human reference genome. Identification and genotyping of genetic variants is typically carried out on short-read data aligned to a single reference, disregarding the underlying variation. RESULTS: We propose a new unified framework for variant calling with short-read data utilizing a representation of human genetic variation - a pan-genomic reference. We provide a modular pipeline that can be seamlessly incorporated into existing sequencing data analysis workflows. Our tool is open source and available online: https://gitlab.com/dvalenzu/PanVC . CONCLUSIONS: Our experiments show that by replacing a standard human reference with a pan-genomic one we achieve an improvement in single-nucleotide variant calling accuracy and in short indel calling accuracy over the widely adopted Genome Analysis Toolkit (GATK) in difficult genomic regions.

A safe and complete algorithm for metagenomic assembly.

BACKGROUND: Reconstructing the genome of a species from short fragments is one of the oldest bioinformatics problems. Metagenomic assembly is a variant of the problem asking to reconstruct the circular genomes of all bacterial species present in a sequencing sample. This problem can be naturally formulated as finding a collection of circular walks of a directed graph G that together cover all nodes, or edges, of G. APPROACH: We address this problem with the "safe and complete" framework of Tomescu and Medvedev (Research in computational Molecular biology-20th annual conference, RECOMB 9649:152-163, 2016). An algorithm is called safe if it returns only those walks (also called safe) that appear as subwalk in all metagenomic assembly solutions for G. A safe algorithm is called complete if it returns all safe walks of G. RESULTS: We give graph-theoretic characterizations of the safe walks of G, and a safe and complete algorithm finding all safe walks of G. In the node-covering case, our algorithm runs in time [Formula: see text], and in the edge-covering case it runs in time [Formula: see text]; n and m denote the number of nodes and edges, respectively, of G. This algorithm constitutes the first theoretical tight upper bound on what can be safely assembled from metagenomic reads using this problem formulation.

Evaluating approaches to find exon chains based on long reads.

Transcript prediction can be modeled as a graph problem where exons are modeled as nodes and reads spanning two or more exons are modeled as exon chains. Pacific Biosciences third-generation sequencing technology produces significantly longer reads than earlier second-generation sequencing technologies, which gives valuable information about longer exon chains in a graph. However, with the high error rates of third-generation sequencing, aligning long reads correctly around the splice sites is a challenging task. Incorrect alignments lead to spurious nodes and arcs in the graph, which in turn lead to incorrect transcript predictions. We survey several approaches to find the exon chains corresponding to long reads in a splicing graph, and experimentally study the performance of these methods using simulated data to allow for sensitivity/precision analysis. Our experiments show that short reads from second-generation sequencing can be used to significantly improve exon chain correctness either by error-correcting the long reads before splicing graph creation, or by using them to create a splicing graph on which the long-read alignments are then projected. We also study the memory and time consumption of various modules, and show that accurate exon chains lead to significantly increased transcript prediction accuracy. AVAILABILITY: The simulated data and in-house scripts used for this article are available at http://www.cs.helsinki.fi/group/gsa/exon-chains/exon-chains-bib.tar.bz2.

Variant genotyping with gap filling.

Although recent developments in DNA sequencing have allowed for great leaps in both the quality and quantity of genome assembly projects, de novo assemblies still lack the efficiency and accuracy required for studying genetic variation of individuals. Thus, efficient and accurate methods for calling and genotyping genetic variants are fundamental to studying the genomes of individuals. We study the problem of genotyping insertion variants. We assume that the location of the insertion is given, and the task is to find the insertion sequence. Insertions are the hardest structural variant to genotype, because the insertion sequence must be assembled from the reads, whereas genotyping other structural variants only requires transformations of the reference genome. The current methods for constructing insertion variants are mostly linked to variation calling methods and are only able to construct small insertions. A sub-problem in genome assembly, the gap filling problem, provides techniques that are readily applicable to insertion genotyping. Gap filling takes the context and length of a missing sequence in a genome assembly and attempts to assemble the intervening sequence. In this paper we show how tools and methods for gap filling can be used to assemble insertion variants by modeling the problem of insertion genotyping as filling gaps in the reference genome. We further give a general read filtering scheme to make the method scalable to large data sets. Our results show that gap filling methods are competitive against insertion genotyping tools. We further show that read filtering improves performance of insertion genotyping especially for long insertions. Our experiments show that on long insertions the new proposed method is the most accurate one, whereas on short insertions it has comparable performance as compared against existing tools.

A framework for space-efficient read clustering in metagenomic samples.

BACKGROUND: A metagenomic sample is a set of DNA fragments, randomly extracted from multiple cells in an environment, belonging to distinct, often unknown species. Unsupervised metagenomic clustering aims at partitioning a metagenomic sample into sets that approximate taxonomic units, without using reference genomes. Since samples are large and steadily growing, space-efficient clustering algorithms are strongly needed. RESULTS: We design and implement a space-efficient algorithmic framework that solves a number of core primitives in unsupervised metagenomic clustering using just the bidirectional Burrows-Wheeler index and a union-find data structure on the set of reads. When run on a sample of total length n, with m reads of maximum length ℓ each, on an alphabet of total size σ, our algorithms take O(n(t+logσ)) time and just 2n+o(n)+O(max{ℓ σlogn,K logm}) bits of space in addition to the index and to the union-find data structure, where K is a measure of the redundancy of the sample and t is the query time of the union-find data structure. CONCLUSIONS: Our experimental results show that our algorithms are practical, they can exploit multiple cores by a parallel traversal of the suffix-link tree, and they are competitive both in space and in time with the state of the art.

Gap Filling as Exact Path Length Problem.

One of the last steps in a genome assembly project is filling the gaps between consecutive contigs in the scaffolds. This problem can be naturally stated as finding an s-t path in a directed graph whose sum of arc costs belongs to a given range (the estimate on the gap length). Here s and t are any two contigs flanking a gap. This problem is known to be NP-hard in general. Here we derive a simpler dynamic programming solution than already known, pseudo-polynomial in the maximum value of the input range. We implemented various practical optimizations to it, and compared our exact gap-filling solution experimentally to popular gap-filling tools. Summing over all the bacterial assemblies considered in our experiments, we can in total fill 76% more gaps than the best previous tool, and the gaps filled by our method span 136% more sequence. Furthermore, the error level of the newly introduced sequence is comparable to that of the previous tools. The experiments also show that our exact approach does not easily scale to larger genomes, where the problem is in general difficult for all tools.

Explaining a Weighted DAG with Few Paths for Solving Genome-Guided Multi-Assembly.

RNA-Seq technology offers new high-throughput ways for transcript identification and quantification based on short reads, and has recently attracted great interest. This is achieved by constructing a weighted DAG whose vertices stand for exons, and whose arcs stand for split alignments of the RNA-Seq reads to the exons. The task consists of finding a number of paths, together with their expression levels, which optimally explain the weights of the graph under various fitting functions, such as least sum of squared residuals. In (Tomescu et al. BMC Bioinformatics, 2013) we studied this genome-guided multi-assembly problem when the number of allowed solution paths was linear in the number of arcs. In this paper, we further refine this problem by asking for a bounded number k of solution paths, which is the setting of most practical interest. We formulate this problem in very broad terms, and show that for many choices of the fitting function it becomes NP-hard. Nevertheless, we identify a natural graph parameter of a DAG G, which we call arc-width and denote ⟨G⟩, and give a dynamic programming algorithm running in time O(W(k)⟨G⟩(k)(⟨G⟩+ k)n) , where n is the number of vertices and W is the maximum weight of G. This implies that the problem is fixed-parameter tractable (FPT) in the parameters W, ⟨G⟩, and k. We also show that the arc-width of DAGs constructed from simulated and real RNA-Seq reads is small in practice. Finally, we study the approximability of this problem, and, in particular, give a fully polynomial-time approximation scheme (FPTAS) for the case when the fitting function penalizes the maximum ratio between the weights of the arcs and their predicted coverage.

Repeat- and error-aware comparison of deletions.

MOTIVATION: The number of reported genetic variants is rapidly growing, empowered by ever faster accumulation of next-generation sequencing data. A major issue is comparability. Standards that address the combined problem of inaccurately predicted breakpoints and repeat-induced ambiguities are missing. This decisively lowers the quality of 'consensus' callsets and hampers the removal of duplicate entries in variant databases, which can have deleterious effects in downstream analyses. RESULTS: We introduce a sound framework for comparison of deletions that captures both tool-induced inaccuracies and repeat-induced ambiguities. We present a maximum matching algorithm that outputs virtual duplicates among two sets of predictions/annotations. We demonstrate that our approach is clearly superior over ad hoc criteria, like overlap, and that it can reduce the redundancy among callsets substantially. We also identify large amounts of duplicate entries in the Database of Genomic Variants, which points out the immediate relevance of our approach. AVAILABILITY AND IMPLEMENTATION: Implementation is open source and available from https://bitbucket.org/readdi/readdi CONTACT: roland.wittler@uni-bielefeld.de or t.marschall@mpi-inf.mpg.de SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.