Prefix-free parsing for building big BWTs

High-throughput sequencing technologies have led to explosive growth of genomic databases; one of which will soon reach hundreds of terabytes. For many applications we want to build and store indexes of these databases but constructing such indexes is a challenge. Fortunately, many of these genomic databases are highly-repetitive - a characteristic that can be exploited to ease the computation of the Burrows-Wheeler Transform (BWT), which underlies many popular indexes. In this paper, we introduce a preprocessing algorithm, referred to as prefix-free parsing, that takes a text T as input, and in one-pass generates a dictionary D and a parse P of T with the property that the BWT of T can be constructed from D and P using workspace proportional to their total size and O(|T|)-time. Our experiments show that D and P are significantly smaller than T in practice, and thus, can fit in a reasonable internal memory even when T is very large. In particular, we show that with prefix-free parsing we can build an 131-MB run-length compressed FM-index (restricted to support only counting and not locating) for 1000 copies of human chromosome 19 in 2 h using 21 GB of memory, suggesting that we can build a 6.73 GB index for 1000 complete human-genome haplotypes in approximately 102 h using about 1 TB of memory

Matching Reads to Many Genomes with the r-Index

The r-index is a tool for compressed indexing of genomic databases for exact pattern matching, which can be used to completely align reads that perfectly match some part of a genome in the database or to find seeds for reads that do not. This article shows how to download and install the programs ri-buildfasta and ri-align; how to call ri-buildfasta on an FASTA file to build an r-index for that file; and how to query that index with ri-align

Efficient Construction of a Complete Index for Pan-Genomics Read Alignment

While short read aligners, which predominantly use the FM-index, are able to easily index one or a few human genomes, they do not scale well to indexing databases containing thousands of genomes. To understand why, it helps to examine the main components of the FM-index in more detail, which is a rank data structure over the Burrows-Wheeler Transform () of the string that will allow us to find the interval in the string\u2019s suffix array () containing pointers to starting positions of occurrences of a given pattern; second, a sample of the that\u2014when used with the rank data structure\u2014allows us access to the . The rank data structure can be kept small even for large genomic databases, by run-length compressing the , but until recently there was no means known to keep the sample small without greatly slowing down access to the . Now that Gagie et al. (SODA 2018) have defined an sample that takes about the same space as the run-length compressed \u2014we have the design for efficient FM-indexes of genomic databases but are faced with the problem of building them. In 2018 we showed how to build the of large genomic databases efficiently (WABI 2018) but the problem of building Gagie et al.\u2019s sample efficiently was left open. We compare our approach to state-of-the-art methods for constructing the sample, and demonstrate that it is the fastest and most space-efficient method on highly repetitive genomic databases. Lastly, we apply our method for indexing partial and whole human genomes and show that it improves over Bowtie with respect to both memory and time

Megadepth: efficient coverage quantification for BigWigs and BAMs

Motivation A common way to summarize sequencing datasets is to quantify data lying within genes or other genomic intervals. This can be slow and can require different tools for different input file types. Results Megadepth is a fast tool for quantifying alignments and coverage for BigWig and BAM/CRAM input files, using substantially less memory than the next-fastest competitor. Megadepth can summarize coverage within all disjoint intervals of the Gencode V35 gene annotation for more than 19 000 GTExV8 BigWig files in approximately 1 h using 32 threads. Megadepth is available both as a command-line tool and as an R/Bioconductor package providing much faster quantification compared to the rtracklayer package. Availability and implementation https://github.com/ChristopherWilks/megadepth, https://bioconductor.org/packages/megadepth. Supplementary information Supplementary data are available at Bioinformatics online

CloudAligner: A fast and full-featured MapReduce based tool for sequence mapping

<p>Abstract</p> <p>Background</p> <p>Research in genetics has developed rapidly recently due to the aid of next generation sequencing (NGS). However, massively-parallel NGS produces enormous amounts of data, which leads to storage, compatibility, scalability, and performance issues. The Cloud Computing and MapReduce framework, which utilizes hundreds or thousands of shared computers to map sequencing reads quickly and efficiently to reference genome sequences, appears to be a very promising solution for these issues. Consequently, it has been adopted by many organizations recently, and the initial results are very promising. However, since these are only initial steps toward this trend, the developed software does not provide adequate primary functions like bisulfite, pair-end mapping, etc., in on-site software such as RMAP or BS Seeker. In addition, existing MapReduce-based applications were not designed to process the long reads produced by the most recent second-generation and third-generation NGS instruments and, therefore, are inefficient. Last, it is difficult for a majority of biologists untrained in programming skills to use these tools because most were developed on Linux with a command line interface.</p> <p>Results</p> <p>To urge the trend of using Cloud technologies in genomics and prepare for advances in second- and third-generation DNA sequencing, we have built a Hadoop MapReduce-based application, CloudAligner, which achieves higher performance, covers most primary features, is more accurate, and has a user-friendly interface. It was also designed to be able to deal with long sequences. The performance gain of CloudAligner over Cloud-based counterparts (35 to 80%) mainly comes from the omission of the reduce phase. In comparison to local-based approaches, the performance gain of CloudAligner is from the partition and parallel processing of the huge reference genome as well as the reads. The source code of CloudAligner is available at <url>http://cloudaligner.sourceforge.net/</url> and its web version is at <url>http://mine.cs.wayne.edu:8080/CloudAligner/.</url></p> <p>Conclusions</p> <p>Our results show that CloudAligner is faster than CloudBurst, provides more accurate results than RMAP, and supports various input as well as output formats. In addition, with the web-based interface, it is easier to use than its counterparts.</p

ZOOM Lite: next-generation sequencing data mapping and visualization software

High-throughput next-generation sequencing technologies pose increasing demands on the efficiency, accuracy and usability of data analysis software. In this article, we present ZOOM Lite, a software for efficient reads mapping and result visualization. With a kernel capable of mapping tens of millions of Illumina or AB SOLiD sequencing reads efficiently and accurately, and an intuitive graphical user interface, ZOOM Lite integrates reads mapping and result visualization into a easy to use pipeline on desktop PC. The software handles both single-end and paired-end reads, and can output both the unique mapping result or the top N mapping results for each read. Additionally, the software takes a variety of input file formats and outputs to several commonly used result formats. The software is freely available at http://bioinfor.com/zoom/lite/

Bitopic binding mode of an M1 muscarinic acetylcholine receptor agonist associated with adverse clinical trial outcomes

The realisation of the therapeutic potential of targeting the M1 muscarinic acetylcholine receptor (M1 mAChR) for the treatment of cognitive decline in Alzheimer's disease has prompted the discovery of M1 mAChR ligands showing efficacy in alleviating cognitive dysfunction in both rodents and humans. Among these is GSK1034702, described previously as a potent M1 receptor allosteric agonist, which showed pro-cognitive effects in rodents and improved immediate memory in a clinical nicotine withdrawal test but induced significant side-effects. Here we provide evidence using ligand binding, chemical biology and functional assays to establish that rather than the allosteric mechanism claimed, GSK1034702 interacts in a bitopic manner at the M1 mAChR such that it can concomitantly span both the orthosteric and an allosteric binding site. The bitopic nature of GSK1034702 together with the intrinsic agonist activity and a lack of muscarinic receptor subtype selectivity reported here, all likely contribute to the adverse effects of this molecule in clinical trials. We conclude that these properties, whilst imparting beneficial effects on learning and memory, are undesirable in a clinical candidate due to the likelihood of adverse side effects. Rather, our data supports the notion that "pure" positive allosteric modulators showing selectivity for the M1 mAChR with low levels of intrinsic activity would be preferable to provide clinical efficacy with low adverse responses

QuasR: quantification and annotation of short reads in R

Summary: QuasR is a package for the integrated analysis of high-throughput sequencing data in R, covering all steps from read preprocessing, alignment and quality control to quantification. QuasR supports different experiment types (including RNA-seq, ChIP-seq and Bis-seq) and analysis variants (e.g. paired-end, stranded, spliced and allele-specific), and is integrated in Bioconductor so that its output can be directly processed for statistical analysis and visualization. Availability and implementation: QuasR is implemented in R and C/C++. Source code and binaries for major platforms (Linux, OS X and MS Windows) are available from Bioconductor (www.bioconductor.org/packages/release/bioc/html/QuasR.html). The package includes a ‘vignette' with step-by-step examples for typical work flows. Contact: [email protected] Supplementary information: Supplementary data are available at Bioinformatics onlin

galign: A Tool for Rapid Genome Polymorphism Discovery

BACKGROUND: Highly parallel sequencing technologies have become important tools in the analysis of sequence polymorphisms on a genomic scale. However, the development of customized software to analyze data produced by these methods has lagged behind. METHODS/PRINCIPAL FINDINGS: Here I describe a tool, 'galign', designed to identify polymorphisms between sequence reads obtained using Illumina/Solexa technology and a reference genome. The 'galign' alignment tool does not use Smith-Waterman matrices for sequence comparisons. Instead, a simple algorithm comparing parsed sequence reads to parsed reference genome sequences is used. 'galign' output is geared towards immediate user application, displaying polymorphism locations, nucleotide changes, and relevant predicted amino-acid changes for ease of information processing. To do so, 'galign' requires several accessory files easily derived from an annotated reference genome. Direct sequencing as well as in silico studies demonstrate that 'galign' provides lesion predictions comparable in accuracy to available prediction programs, accompanied by greater processing speed and more user-friendly output. We demonstrate the use of 'galign' to identify mutations leading to phenotypic consequences in C. elegans. CONCLUSION/SIGNIFICANCE: Our studies suggest that 'galign' is a useful tool for polymorphism discovery, and is of immediate utility for sequence mining in C. elegans