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1. Vargas: heuristic-free alignment for assessing linear and graph read aligners

   https://doi.org/10.1093/bioinformatics/btaa265  

   Darby, Charlotte A ; Gaddipati, Ravi ; Schatz, Michael C ; Langmead, Ben ; Birol, Inanc ( April 2020 , Bioinformatics)

   Abstract Motivation Read alignment is central to many aspects of modern genomics. Most aligners use heuristics to accelerate processing, but these heuristics can fail to find the optimal alignments of reads. Alignment accuracy is typically measured through simulated reads; however, the simulated location may not be the (only) location with the optimal alignment score. Results Vargas implements a heuristic-free algorithm guaranteed to find the highest-scoring alignment for real sequencing reads to a linear or graph genome. With semiglobal and local alignment modes and affine gap and quality-scaled mismatch penalties, it can implement the scoring functions of commonly used aligners to calculate optimal alignments. While this is computationally intensive, Vargas uses multi-core parallelization and vectorized (SIMD) instructions to make it practical to optimally align large numbers of reads, achieving a maximum speed of 456 billion cell updates per second. We demonstrate how these “gold standard” Vargas alignments can be used to improve heuristic alignment accuracy by optimizing command-line parameters in Bowtie 2, BWA-MEM, and vg to align more reads correctly. Availability and implementation Source code implemented in C ++ and compiled binary releases are available at https://github.com/langmead-lab/vargas under the MIT license. Supplementary information Supplementary data are available at Bioinformatics online. 

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2. UPP2: fast and accurate alignment of datasets with fragmentary sequences

   https://doi.org/10.1093/bioinformatics/btad007  

   Park, Minhyuk ; Ivanovic, Stefan ; Chu, Gillian ; Shen, Chengze ; Warnow, Tandy ; Marschall, ed., Tobias ( January 2023 , Bioinformatics)

   Multiple sequence alignment (MSA) is a basic step in many bioinformatics pipelines. However, achieving highly accurate alignments on large datasets, especially those with sequence length heterogeneity, is a challenging task. Ultra-large multiple sequence alignment using Phylogeny-aware Profiles (UPP) is a method for MSA estimation that builds an ensemble of Hidden Markov Models (eHMM) to represent an estimated alignment on the full-length sequences in the input, and then adds the remaining sequences into the alignment using selected HMMs in the ensemble. Although UPP provides good accuracy, it is computationally intensive on large datasets.

   We present UPP2, a direct improvement on UPP. The main advance is a fast technique for selecting HMMs in the ensemble that allows us to achieve the same accuracy as UPP but with greatly reduced runtime. We show that UPP2 produces more accurate alignments compared to leading MSA methods on datasets exhibiting substantial sequence length heterogeneity and is among the most accurate otherwise.

   https://github.com/gillichu/sepp.

   Supplementary data are available at Bioinformatics online.

    

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3. Phyx: phylogenetic tools for unix

   https://doi.org/10.1093/bioinformatics/btx063  

   Brown, Joseph W. ; Walker, Joseph F. ; Smith, Stephen A. ; Kelso, ed., Janet ( February 2017 , Bioinformatics)

   The ease with which phylogenomic data can be generated has drastically escalated the computational burden for even routine phylogenetic investigations. To address this, we present phyx: a collection of programs written in C ++ to explore, manipulate, analyze and simulate phylogenetic objects (alignments, trees and MCMC logs). Modelled after Unix/GNU/Linux command line tools, individual programs perform a single task and operate on standard I/O streams that can be piped to quickly and easily form complex analytical pipelines. Because of the stream-centric paradigm, memory requirements are minimized (often only a single tree or sequence in memory at any instance), and hence phyx is capable of efficiently processing very large datasets.

   phyx runs on POSIX-compliant operating systems. Source code, installation instructions, documentation and example files are freely available under the GNU General Public License at https://github.com/FePhyFoFum/phyx

   Supplementary data are available at Bioinformatics online.

    

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4. HMMerge: an ensemble method for multiple sequence alignment

   https://doi.org/10.1093/bioadv/vbad052  

   Park, Minhyuk ; Warnow, Tandy ; Lengauer, ed., Thomas ( April 2023 , Bioinformatics Advances)

   Despite advances in method development for multiple sequence alignment over the last several decades, the alignment of datasets exhibiting substantial sequence length heterogeneity, especially when the input sequences include very short sequences (either as a result of sequencing technologies or of large deletions during evolution) remains an inadequately solved problem.

   We present HMMerge, a method to compute an alignment of datasets exhibiting high sequence length heterogeneity, or to add short sequences into a given ‘backbone’ alignment. HMMerge builds on the technique from its predecessor alignment methods, UPP and WITCH, which build an ensemble of profile HMMs to represent the backbone alignment and add the remaining sequences into the backbone alignment using the ensemble. HMMerge differs from UPP and WITCH by building a new ‘merged’ HMM from the ensemble, and then using that merged HMM to align the query sequences. We show that HMMerge is competitive with WITCH, with an advantage over WITCH when adding very short sequences into backbone alignments.

   HMMerge is freely available at https://github.com/MinhyukPark/HMMerge.

   Supplementary data are available at Bioinformatics Advances online.

    

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5. LexicHash: sequence similarity estimation via lexicographic comparison of hashes

   https://doi.org/10.1093/bioinformatics/btad652  

   Greenberg, Grant ; Ravi, Aditya Narayan ; Shomorony, Ilan ( November 2023 , Bioinformatics)

   Alkan, Can (Ed.)

   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.

   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.

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

    

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