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Lambda3: homology search for protein, nucleotide and bisulfite-converted sequences
Abstract
Motivation
Local alignments of query sequences in large databases represent a core part of metagenomic studies and facilitate homology search. Following the development of NCBI Blast, many applications aimed to provide faster and equally sensitive local alignment frameworks. Most applications focus on protein alignments, while only few also facilitate DNA-based searches. None of the established programs allow searching DNA sequences from bisulfite sequencing experiments commonly used for DNA methylation profiling, for which specific alignment strategies need to be implemented.
Results
Here, we introduce Lambda3, a new version of the local alignment application Lambda. Lambda3 is the first solution that enables the search of protein, nucleotide as well as bisulfite-converted nucleotide query sequences. Its protein mode achieves comparable performance to that of the highly optimized protein alignment application Diamond, while the nucleotide mode consistently outperforms established local nucleotide aligners. Combined, Lambda3 presents a universal local alignment framework that enables fast and sensitive homology searches for a wide range of use-cases.
Availability and implementation
Lambda3 is free and open-source software publicly available at https://github.com/seqan/lambda/.
ResearchGate has not been able to resolve any citations for this publication.
Proteins are the executors of cellular physiological activities, and accurate structural and function elucidation is crucial for the refined mapping of proteins. As a feature engineering method, the reduction of amino acid composition is not only an important method for protein structure and function analysis, but also opens a broad horizon for the complex field of machine learning. Representing sequences with fewer amino acid types greatly reduces the complexity and noise of traditional feature engineering in dimension, and provides more interpretable predictive models for machine learning to capture key features. In this paper, we systematically reviewed the strategy and method studies of the reduced amino acid (RAA) alphabets, and summarized its main research in protein sequence alignment, functional classification, and prediction of structural properties, respectively. In the end, we gave a comprehensive analysis of 672 RAA alphabets from 74 reduction methods.
Evaluating metagenomic software is key for optimizing metagenome interpretation and focus of the Initiative for the Critical Assessment of Metagenome Interpretation (CAMI). The CAMI II challenge engaged the community to assess methods on realistic and complex datasets with long- and short-read sequences, created computationally from around 1,700 new and known genomes, as well as 600 new plasmids and viruses. Here we analyze 5,002 results by 76 program versions. Substantial improvements were seen in assembly, some due to long-read data. Related strains still were challenging for assembly and genome recovery through binning, as was assembly quality for the latter. Profilers markedly matured, with taxon profilers and binners excelling at higher bacterial ranks, but underperforming for viruses and Archaea. Clinical pathogen detection results revealed a need to improve reproducibility. Runtime and memory usage analyses identified efficient programs, including top performers with other metrics. The results identify challenges and guide researchers in selecting methods for analyses. This study presents the results of the second round of the Critical Assessment of Metagenome Interpretation challenges (CAMI II), which is a community-driven effort for comprehensively benchmarking tools for metagenomics data analysis.
We present Raptor, a system for approximately searching many queries like NGS reads or transcripts in large collections of nucleotide sequences. Raptor uses winnowing minimizers to define a set of representative k-mers, an extension of the Interleaved Bloom Filters (IBF) as a set membership data structure, and probabilistic thresholding for minimizers. Our approach allows compression and partitioning of the IBF to enable the effective use of secondary memory. We test and show the performance and limitations of the new features using simulated and real data sets. Our data structure can be used to accelerate various core bioinformatics applications. We show this by re-implementing the distributed read mapping tool DREAM-Yara.
We are at the beginning of a genomic revolution in which all known species are planned to be sequenced. Accessing such data for comparative analyses is crucial in this new age of data-driven biology. Here, we introduce an improved version of DIAMOND that greatly exceeds previous search performances and harnesses supercomputing to perform tree-of-life scale protein alignments in hours, while matching the sensitivity of the gold standard BLASTP. An updated version of DIAMOND uses improved algorithmic procedures and a customized high-performance computing framework to make seemingly prohibitive large-scale protein sequence alignments feasible.
Circulating cell-free methyl-DNA (mcfDNA) contains promising cancer markers but its low abundance and possibly diverse origin pose challenges toward the accurate diagnosis of early stage cancers. By whole-genome bisulfite sequencing (WGBS) of cell-free DNA (cfDNA) from about 0.5 mL plasma of mice xenografted with human tumors, we obtained and aligned the reads to the human genome, filtered out the mouse and carrier bacterial sequences, and confirmed the tumor origin of methyl-cfDNA (mctDNA) by methylation-sensitive restriction enzyme digestion prior to species-specific PCR. We estimated that human tumor-specific reads (ctDNA) or mctDNA comprised about 0.29 or 0.01%, respectively of the xenograft mouse cfDNA, and about 0.029 or 0.001% of the cfDNA of human early stage cancer patients. Similar WGBS of early stage (0-II, node- and metastasis-free) breast, lung or colorectal cancer samples identified hundreds of specific DMRs (differentially methylated regions) compared to healthy controls. Their association with tumourigenesis was supported by stage-dependent methylation, tumor suppressor or oncogene clusters, and genes also identified in the xenograft samples. Using 20 three-cancer-common and 17 colorectal cancer-specific DMRs in combination (top 0.0018% of the WGBS methylation clusters) was sufficient to distinguish the stage I colorectal cancers from breast and lung cancers and healthy controls. Our data thus confirmed the tumor origin of mctDNA by sequence specificity, and provide a selection threshold for authentic tumor mctDNA markers toward precise diagnosis of early stage cancers solely by top DMRs in combination.
Whole genome bisulfite sequencing is currently at the forefront of epigenetic analysis, facilitating the nucleotide-level resolution of 5-methylcytosine (5mC) on a genome-wide scale. Specialized software have been developed to accommodate the unique difficulties in aligning such sequencing reads to a given reference, building on the knowledge acquired from model organisms such as human, or Arabidopsis thaliana. As the field of epigenetics expands its purview to non-model plant species, new challenges arise which bring into question the suitability of previously established tools. Herein, nine short-read aligners are evaluated: Bismark, BS-Seeker2, BSMAP, BWA-meth, ERNE-BS5, GEM3, GSNAP, Last and segemehl. Precision-recall of simulated alignments, in comparison to real sequencing data obtained from three natural accessions, reveals on-balance that BWA-meth and BSMAP are able to make the best use of the data during mapping. The influence of difficult-to-map regions, characterized by deviations in sequencing depth over repeat annotations, is evaluated in terms of the mean absolute deviation of the resulting methylation calls in comparison to a realistic methylome. Downstream methylation analysis is responsive to the handling of multi-mapping reads relative to mapping quality (MAPQ), and potentially susceptible to bias arising from the increased sequence complexity of densely methylated reads.
Colorectal cancer is a heterogenous and mostly sporadic disease, the development of which is associated with microbial dysbiosis. Recent advances in subtype classification have successfully stratified the disease using molecular profiling. To understand potential relationships between molecular mechanisms differentiating the subtypes of colorectal cancer and composition of gut microbial community, we classified a set of 34 tumour samples into molecular subtypes using RNA-sequencing gene expression profiles and determined relative abundances of bacterial taxonomic groups. To identify bacterial community composition, 16S rRNA amplicon metabarcoding was used as well as whole genome metagenomics of the non-human part of RNA-sequencing data. The generated data expands the collection of the data sources related to the disease and connects molecular aspects of the cancer with environmental impact of microbial community. Machine-accessible metadata file describing the reported data (ISA-Tab format)
The generation of thousands of fungal genomes is leading to a better understanding of genes and genomic organization within the kingdom. However, the epigenome, which includes DNA and chromatin modifications, remains poorly investigated in fungi. Large comparative studies in animals and plants have deepened our understanding of epigenomic variation, particularly of the modified base 5-methylcytosine (5mC), but taxonomic sampling of disparate groups is needed to develop unifying explanations for 5mC variation. Here, we utilize the largest phylogenetic resolution of 5mC methyltransferases (5mC MTases) and genome evolution to better understand levels and patterns of 5mC across fungi. We show that extant 5mC MTase genotypes are descendent from ancestral maintenance and de novo genotypes, whereas the 5mC MTases DIM-2 and RID are more recently derived, and that 5mC levels are correlated with 5mC MTase genotype and transposon content. Our survey also revealed that fungi lack canonical gene-body methylation, which distinguishes fungal epigenomes from certain insect and plant species. However, some fungal species possess independently derived clusters of contiguous 5mC encompassing many genes. In some cases, DNA repair pathways and the N⁶-methyladenine DNA modification negatively coevolved with 5mC pathways, which additionally contributed to interspecific epigenomic variation across fungi.
Motivation:
Mapping-based approaches have become limited in their application to very large sets of references since computing an FM-index for very large databases (e.g. >10 GB) has become a bottleneck. This affects many analyses that need such index as an essential step for approximate matching of the NGS reads to reference databases. For instance, in typical metagenomics analysis, the size of the reference sequences has become prohibitive to compute a single full-text index on standard machines. Even on large memory machines, computing such index takes about 1 day of computing time. As a result, updates of indices are rarely performed. Hence, it is desirable to create an alternative way of indexing while preserving fast search times.
Results:
To solve the index construction and update problem we propose the DREAM (Dynamic seaRchablE pArallel coMpressed index) framework and provide an implementation. The main contributions are the introduction of an approximate search distributor via a novel use of Bloom filters. We combine several Bloom filters to form an interleaved Bloom filter and use this new data structure to quickly exclude reads for parts of the databases where they cannot match. This allows us to keep the databases in several indices which can be easily rebuilt if parts are updated while maintaining a fast search time. The second main contribution is an implementation of DREAM-Yara a distributed version of a fully sensitive read mapper under the DREAM framework.
Availability and implementation:
https://gitlab.com/pirovc/dream_yara/.
Soils harbour some of the most diverse microbiomes on Earth
and are essential for both nutrient cycling and carbon storage.
To understand soil functioning, it is necessary to model the
global distribution patterns and functional gene repertoires of
soil microorganisms, as well as the biotic and environmental
associations between the diversity and structure of both bacterial
and fungal soil communities1–4. Here we show, by leveraging
metagenomics and metabarcoding of global topsoil samples (189
sites, 7,560 subsamples), that bacterial, but not fungal, genetic
diversity is highest in temperate habitats and that microbial gene
composition varies more strongly with environmental variables than
with geographic distance. We demonstrate that fungi and bacteria
show global niche differentiation that is associated with contrasting
diversity responses to precipitation and soil pH. Furthermore, we
provide evidence for strong bacterial–fungal antagonism, inferred
from antibiotic-resistance genes, in topsoil and ocean habitats,
indicating the substantial role of biotic interactions in shaping
microbial communities. Our results suggest that both competition
and environmental filtering affect the abundance, composition
and encoded gene functions of bacterial and fungal communities,
indicating that the relative contributions of these microorganisms
to global nutrient cycling varies spatially.
Indigenous populations of the Americas experienced high mortality rates during the early contact period as a result of infectious diseases, many of which were introduced by Europeans. Most of the pathogenic agents that caused these outbreaks remain unknown. Through the introduction of a new metagenomic analysis tool called MALT, applied here to search for traces of ancient pathogen DNA, we were able to identify Salmonella enterica in individuals buried in an early contact era epidemic cemetery at Teposcolula-Yucundaa, Oaxaca in southern Mexico. This cemetery is linked, based on historical and archaeological evidence, to the 1545-1550 CE epidemic that affected large parts of Mexico. Locally, this epidemic was known as 'cocoliztli', the pathogenic cause of which has been debated for more than a century. Here, we present genome-wide data from ten individuals for Salmonella enterica subsp. enterica serovar Paratyphi C, a bacterial cause of enteric fever. We propose that S. Paratyphi C be considered a strong candidate for the epidemic population decline during the 1545 cocoliztli outbreak at Teposcolula-Yucundaa.
Background:
A number of clinico-pathological criteria and molecular profiles have been used to stratify patients into high- and low-risk groups. Currently, there are still no effective methods to determine which patients harbor micrometastatic disease after standard breast cancer therapy and who will eventually develop local or distant recurrence. The purpose of our study was to identify circulating DNA methylation changes that can be used for prediction of metastatic breast cancer (MBC).
Results:
Differential methylation analysis revealed ~5.0 × 10(6) differentially methylated CpG loci in MBC compared with healthy individuals (H) or disease-free survivors (DFS). In contrast, there was a strong degree of similarity between H and DFS. Overall, MBC demonstrated global hypomethylation and focal CpG island (CPGI) hypermethylation. Data analysis identified 21 novel hotspots, within CpG islands, that differed most dramatically in MBC compared with H or DFS.
Conclusions:
This unbiased analysis of cell-free (cf) DNA identified 21 DNA hypermethylation hotspots associated with MBC and demonstrated the ability to distinguish tumor-specific changes from normal-derived signals at the whole-genome level. This signature is a potential blood-based biomarker that could be advantageous at the time of surgery and/or after the completion of chemotherapy to indicate patients with micrometastatic disease who are at a high risk of recurrence and who could benefit from additional therapy.
High-throughput DNA sequencing has considerably changed the possibilities for conducting biomedical research by measuring billions of short DNA or RNA fragments. A central computational problem, and for many applications a first step, consists of determining where the fragments came from in the original genome. In this article, we review the main techniques for generating the fragments, the main applications, and the main algorithmic ideas for computing a solution to the read alignment problem. In addition, we describe pitfalls and difficulties connected to determining the correct positions of reads. Expected final online publication date for the Annual Review of Genomics and Human Genetics Volume 16 is August 31, 2015. Please see http://www.annualreviews.org/catalog/pubdates.aspx for revised estimates.
Motivation: Next-generation sequencing technologies produce unprecedented amounts of data, leading to completely new research fields. One of these is metagenomics, the study of large-size DNA samples containing a multitude of diverse organisms. A key problem in metagenomics is to functionally and taxonomically classify the sequenced DNA, to which end the well-known BLAST program is usually used. But BLAST has dramatic resource requirements at metagenomic scales of data, imposing a high financial or technical burden on the researcher. Multiple attempts have been made to overcome these limitations and present a viable alternative to BLAST.
Results: In this work we present Lambda, our own alternative for BLAST in the context of sequence classification. In our tests, Lambda often outperforms the best tools at reproducing BLAST’s results and is the fastest compared with the current state of the art at comparable levels of sensitivity.
Availability and implementation: Lambda was implemented in the SeqAn open-source C++ library for sequence analysis and is publicly available for download at http://www.seqan.de/projects/lambda.
Contact:
hannes.hauswedell@fu-berlin.de
Supplementary information:
Supplementary data are available at Bioinformatics online.
Coupling bisulfite conversion with next-generation sequencing (Bisulfite-seq) enables genome-wide measurement of DNA methylation, but poses unique challenges for mapping. However, despite a proliferation of Bisulfite-seq mapping tools, no systematic comparison of their genomic coverage and quantitative accuracy has been reported. We sequenced bisulfite-converted DNA from two tissues from each of two healthy human adults and systematically compared five widely used Bisulfite-seq mapping algorithms: Bismark, BSMAP, Pash, BatMeth and BS Seeker. We evaluated their computational speed and genomic coverage and verified their percentage methylation estimates. With the exception of BatMeth, all mappers covered >70% of CpG sites genome-wide and yielded highly concordant estimates of percentage methylation (r(2) ≥ 0.95). Fourfold variation in mapping time was found between BSMAP (fastest) and Pash (slowest). In each library, 8-12% of genomic regions covered by Bismark and Pash were not covered by BSMAP. An experiment using simulated reads confirmed that Pash has an exceptional ability to uniquely map reads in genomic regions of structural variation. Independent verification by bisulfite pyrosequencing generally confirmed the percentage methylation estimates by the mappers. Of these algorithms, Bismark provides an attractive combination of processing speed, genomic coverage and quantitative accuracy, whereas Pash offers considerably higher genomic coverage.
In the context of metagenomics, we introduce a new approach to protein database search called PAUDA, which runs ∼10 000 times faster than BLASTX, while achieving about one-third of the assignment rate of reads to KEGG orthology groups, and producing gene and taxon abundance profiles that are highly correlated to those obtained with BLASTX. PAUDA requires <80 CPU hours to analyze a dataset of 246 million Illumina DNA reads from permafrost soil for which a previous BLASTX analysis (on a subset of 176 million reads) reportedly required 800 000 CPU hours, leading to the same clustering of samples by functional profiles.
Availability: PAUDA is freely available from: http://ab.inf.uni-tuebingen.de/software/pauda. Also supplementary method details are available from this website.
Contact:
daniel.huson@uni-tuebingen.de or xiechao@bic.nus.edu.sg
A variety of microbial communities and their genes (the microbiome) exist throughout the human body, with fundamental roles in human health and disease. The National Institutes of Health (NIH)-funded Human Microbiome Project Consortium has established a population-scale framework to develop metagenomic protocols, resulting in a broad range of quality-controlled resources and data including standardized methods for creating, processing and interpreting distinct types of high-throughput metagenomic data available to the scientific community. Here we present resources from a population of 242 healthy adults sampled at 15 or 18 body sites up to three times, which have generated 5,177 microbial taxonomic profiles from 16S ribosomal RNA genes and over 3.5 terabases of metagenomic sequence so far. In parallel, approximately 800 reference strains isolated from the human body have been sequenced. Collectively, these data represent the largest resource describing the abundance and variety of the human microbiome, while providing a framework for current and future studies.
Cytosine DNA methylation is one of the major epigenetic modifications and influences gene expression, developmental processes, X-chromosome inactivation, and genomic imprinting. Aberrant methylation is furthermore known to be associated with several diseases including cancer. The gold standard to determine DNA methylation on genome-wide scales is 'bisulfite sequencing': DNA fragments are treated with sodium bisulfite resulting in the conversion of unmethylated cytosines into uracils, whereas methylated cytosines remain unchanged. The resulting sequencing reads thus exhibit asymmetric bisulfite-related mismatches and suffer from an effective reduction of the alphabet size in the unmethylated regions, rendering the mapping of bisulfite sequencing reads computationally much more demanding. As a consequence, currently available read mapping software often fails to achieve high sensitivity and in many cases requires unrealistic computational resources to cope with large real-life datasets.
In this study, we present a seed-based approach based on enhanced suffix arrays in conjunction with Myers bit-vector algorithm to efficiently extend seeds to optimal semi-global alignments while allowing for bisulfite-related substitutions. It outperforms most current approaches in terms of sensitivity and performs time-competitive in mapping hundreds of millions of sequencing reads to vertebrate genomes.
The software segemehl is freely available at http://www.bioinf.uni-leipzig.de/Software/segemehl.
Next Generation Sequencing (NGS) is producing enormous corpuses of short DNA reads, affecting emerging fields like metagenomics. Protein similarity search--a key step to achieve annotation of protein-coding genes in these short reads, and identification of their biological functions--faces daunting challenges because of the very sizes of the short read datasets.
We developed a fast protein similarity search tool RAPSearch that utilizes a reduced amino acid alphabet and suffix array to detect seeds of flexible length. For short reads (translated in 6 frames) we tested, RAPSearch achieved ~20-90 times speedup as compared to BLASTX. RAPSearch missed only a small fraction (~1.3-3.2%) of BLASTX similarity hits, but it also discovered additional homologous proteins (~0.3-2.1%) that BLASTX missed. By contrast, BLAT, a tool that is even slightly faster than RAPSearch, had significant loss of sensitivity as compared to RAPSearch and BLAST.
RAPSearch is implemented as open-source software and is accessible at http://omics.informatics.indiana.edu/mg/RAPSearch. It enables faster protein similarity search. The application of RAPSearch in metageomics has also been demonstrated.
A combination of bisulfite treatment of DNA and high-throughput sequencing (BS-Seq) can capture a snapshot of a cell's epigenomic state by revealing its genome-wide cytosine methylation at single base resolution. Bismark is a flexible tool for the time-efficient analysis of BS-Seq data which performs both read mapping and methylation calling in a single convenient step. Its output discriminates between cytosines in CpG, CHG and CHH context and enables bench scientists to visualize and interpret their methylation data soon after the sequencing run is completed.
Availability and implementation: Bismark is released under the GNU GPLv3+ licence. The source code is freely available from www.bioinformatics.bbsrc.ac.uk/projects/bismark/.
Contact: felix.krueger@bbsrc.ac.uk
Supplementary information: Supplementary data are available at Bioinformatics online.
PSI-BLAST is an iterative program to search a database for proteins with distant similarity to a query sequence. We investigated
over a dozen modifications to the methods used in PSI-BLAST, with the goal of improving accuracy in finding true positive
matches. To evaluate performance we used a set of 103 queries for which the true positives in yeast had been annotated by
human experts, and a popular measure of retrieval accuracy (ROC) that can be normalized to take on values between 0 (worst)
and 1 (best). The modifications we consider novel improve the ROC score from 0.758 ± 0.005 to 0.895 ± 0.003. This does not
include the benefits from four modifications we included in the ‘baseline’ version, even though they were not implemented
in PSI-BLAST version 2.0. The improvement in accuracy was confirmed on a small second test set. This test involved analyzing
three protein families with curated lists of true positives from the non-redundant protein database. The modification that
accounts for the majority of the improvement is the use, for each database sequence, of a position-specific scoring system
tuned to that sequence’s amino acid composition. The use of composition-based statistics is particularly beneficial for large-scale
automated applications of PSI-BLAST.
The modulation of DNA-protein interactions by methylation of protein-binding sites in DNA and the occurrence in genomic imprinting, X chromosome inactivation, and fragile X syndrome of different methylation patterns in DNA of different chromosomal origin have underlined the need to establish methylation patterns in individual strands of particular genomic sequences. We report a genomic sequencing method that provides positive identification of 5-methylcytosine residues and yields strand-specific sequences of individual molecules in genomic DNA. The method utilizes bisulfite-induced modification of genomic DNA, under conditions whereby cytosine is converted to uracil, but 5-methylcytosine remains nonreactive. The sequence under investigation is then amplified by PCR with two sets of strand-specific primers to yield a pair of fragments, one from each strand, in which all uracil and thymine residues have been amplified as thymine and only 5-methylcytosine residues have been amplified as cytosine. The PCR products can be sequenced directly to provide a strand-specific average sequence for the population of molecules or can be cloned and sequenced to provide methylation maps of single DNA molecules. We tested the method by defining the methylation status within single DNA strands of two closely spaced CpG dinucleotides in the promoter of the human kininogen gene. During the analysis, we encountered in sperm DNA an unusual methylation pattern, which suggests that the high methylation level of single-copy sequences in sperm may be locally modulated by binding of protein factors in germ-line cells.
A new approach to rapid sequence comparison, basic local alignment search tool (BLAST), directly approximates alignments that optimize a measure of local similarity, the maximal segment pair (MSP) score. Recent mathematical results on the stochastic properties of MSP scores allow an analysis of the performance of this method as well as the statistical significance of alignments it generates. The basic algorithm is simple and robust; it can be implemented in a number of ways and applied in a variety of contexts including straightforward DNA and protein sequence database searches, motif searches, gene identification searches, and in the analysis of multiple regions of similarity in long DNA sequences. In addition to its flexibility and tractability to mathematical analysis, BLAST is an order of magnitude faster than existing sequence comparison tools of comparable sensitivity.
An unusual pattern in a nucleic acid or protein sequence or a region of strong similarity shared by two or more sequences may have biological significance. It is therefore desirable to know whether such a pattern can have arisen simply by chance. To identify interesting sequence patterns, appropriate scoring values can be assigned to the individual residues of a single sequence or to sets of residues when several sequences are compared. For single sequences, such scores can reflect biophysical properties such as charge, volume, hydrophobicity, or secondary structure potential; for multiple sequences, they can reflect nucleotide or amino acid similarity measured in a wide variety of ways. Using an appropriate random model, we present a theory that provides precise numerical formulas for assessing the statistical significance of any region with high aggregate score. A second class of results describes the composition of high-scoring segments. In certain contexts, these permit the choice of scoring systems which are "optimal" for distinguishing biologically relevant patterns. Examples are given of applications of the theory to a variety of protein sequences, highlighting segments with unusual biological features. These include distinctive charge regions in transcription factors and protooncogene products, pronounced hydrophobic segments in various receptor and transport proteins, and statistically significant subalignments involving the recently characterized cystic fibrosis gene.
Protein design experiments have shown that the use of specific subsets of amino acids can produce foldable proteins. This prompts the question of whether there is a minimal amino acid alphabet which could be used to fold all proteins. In this work we make an analogy between sequence patterns which produce foldable sequences and those which make it possible to detect structural homologs by aligning sequences, and use it to suggest the possible size of such a reduced alphabet. We estimate that reduced alphabets containing 10-12 letters can be used to design foldable sequences for a large number of protein families. This estimate is based on the observation that there is little loss of the information necessary to pick out structural homologs in a clustered protein sequence database when a suitable reduction of the amino acid alphabet from 20 to 10 letters is made, but that this information is rapidly degraded when further reductions in the alphabet are made.
Motivation:
Amino acid substitution matrices play a central role in protein alignment methods. Standard log-odds matrices, such as those of the PAM and BLOSUM series, are constructed from large sets of protein alignments having implicit background amino acid frequencies. However, these matrices frequently are used to compare proteins with markedly different amino acid compositions, such as transmembrane proteins or proteins from organisms with strongly biased nucleotide compositions. It has been argued elsewhere that standard matrices are not ideal for such comparisons and, furthermore, a rationale has been presented for transforming a standard matrix for use in a non-standard compositional context.
Results:
This paper presents the mathematical details underlying the compositional adjustment of amino acid or DNA substitution matrices.
Although genomics has classically focused on pure, easy-to-obtain samples, such as microbes that grow readily in culture or large animals and plants, these organisms represent only a fraction of the living or once-living organisms of interest. Many species are difficult to study in isolation because they fail to grow in laboratory culture, depend on other organisms for critical processes, or have become extinct. Methods that are based on DNA sequencing circumvent these obstacles, as DNA can be isolated directly from living or dead cells in various contexts. Such methods have led to the emergence of a new field, which is referred to as metagenomics.
Cytosine DNA methylation is important in regulating gene expression and in silencing transposons and other repetitive sequences. Recent genomic studies in Arabidopsis thaliana have revealed that many endogenous genes are methylated either within their promoters or within their transcribed regions, and that gene methylation is highly correlated with transcription levels. However, plants have different types of methylation controlled by different genetic pathways, and detailed information on the methylation status of each cytosine in any given genome is lacking. To this end, we generated a map at single-base-pair resolution of methylated cytosines for Arabidopsis, by combining bisulphite treatment of genomic DNA with ultra-high-throughput sequencing using the Illumina 1G Genome Analyser and Solexa sequencing technology. This approach, termed BS-Seq, unlike previous microarray-based methods, allows one to sensitively measure cytosine methylation on a genome-wide scale within specific sequence contexts. Here we describe methylation on previously inaccessible components of the genome and analyse the DNA methylation sequence composition and distribution. We also describe the effect of various DNA methylation mutants on genome-wide methylation patterns, and demonstrate that our newly developed library construction and computational methods can be applied to large genomes such as that of mouse.
Motivation:
Pairwise sequence alignment is undoubtedly a central tool in many bioinformatics analyses. In this paper, we present a generically accelerated module for pairwise sequence alignments applicable for a broad range of applications. In our module, we unified the standard dynamic programming kernel used for pairwise sequence alignments and extended it with a generalized inter-sequence vectorization layout, such that many alignments can be computed simultaneously by exploiting SIMD (single instruction multiple data) instructions of modern processors. We then extended the module by adding two layers of thread-level parallelization, where we (a) distribute many independent alignments on multiple threads and (b) inherently parallelize a single alignment computation using a work stealing approach producing a dynamic wavefront progressing along the minor diagonal.
Results:
We evaluated our alignment vectorization and parallelization on different processors, including the newest Intel® Xeon® (Skylake) and Intel® Xeon PhiTM (KNL) processors, and use cases. The instruction set AVX512-BW (Byte and Word), available on Skylake processors, can genuinely improve the performance of vectorized alignments. We could run single alignments 1600 times faster on the Xeon PhiTM and 1400 times faster on the Xeon® than executing them with our previous sequential alignment module.
Availability and implementation:
The module is programmed in C++ using the SeqAn (Reinert et al., 2017) library and distributed with version 2.4 under the BSD license. We support SSE4, AVX2, AVX512 instructions and included UME: SIMD, a SIMD-instruction wrapper library, to extend our module for further instruction sets. We thoroughly test all alignment components with all major C++ compilers on various platforms.
Supplementary information:
Supplementary data are available at Bioinformatics online.
Background:
The use of novel algorithmic techniques is pivotal to many important problems in life science. For example the sequencing of the human genome Venter et al. (2001) would not have been possible without advanced assembly algorithms and the development of practical BWT based read mappers have been instrumental for NGS analysis. However, owing to the high speed of technological progress and the urgent need for bioinformatics tools, there was a widening gap between state-of-the-art algorithmic techniques and the actual algorithmic components of tools that are in widespread use. We previously addressed this by introducing the SeqAn library of efficient data types and algorithms in 2008 Döring et al. (2008).
Results:
The SeqAn library has matured considerably since its first publication 9 years ago. In this article we review its status as an established resource for programmers in the field of sequence analysis and its contributions to many analysis tools.
Conclusions:
We anticipate that SeqAn will continue to be a valuable resource, especially since it started to actively support various hardware acceleration techniques in a systematic manner.
Significance
Through massive shotgun sequencing of circulating cell-free DNA from the blood of more than 1,000 independent samples, we identified hundreds of new bacteria and viruses which represent previously unidentified members of the human microbiome. Previous studies targeted specific niches such as feces, skin, or the oral cavity, whereas our approach of using blood effectively enables sampling of the entire body and reveals the colonization of niches which have been previously inaccessible. We were thus able to discover that the human body contains a vast and unexpected diversity of microbes, many of which have highly divergent relationships to the known tree of life.
The alignment of sequencing reads against a protein reference database is a major computational bottleneck in metagenomics and data-intensive evolutionary projects. Although recent tools offer improved performance over the gold standard BLASTX, they exhibit only a modest speedup or low sensitivity. We introduce DIAMOND, an open-source algorithm based on double indexing that is 20,000 times faster than BLASTX on short reads and has a similar degree of sensitivity. © 2014 Nature Publishing Group, a division of Macmillan Publishers Limited. All Rights Reserved.
Sequence similarity searching, typically with BLAST, is the most widely used and most reliable strategy for characterizing newly determined sequences. Sequence similarity searches can identify "homologous" proteins or genes by detecting excess similarity- statistically significant similarity that reflects common ancestry. This unit provides an overview of the inference of homology from significant similarity, and introduces other units in this chapter that provide more details on effective strategies for identifying homologs. Curr. Protoc. Bioinform. 42:3.1.1-3.1.8. © 2013 by John Wiley & Sons, Inc.
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It is well known that there are some similarities among various naturally occurring amino acids. Thus, the complexity in protein systems could be reduced by sorting these amino acids with similarities into groups and then protein sequences can be simplified by reduced alphabets. This paper discusses how to group similar amino acids and whether there is a minimal amino acid alphabet by which proteins can be folded. Various reduced alphabets are obtained by reserving the maximal information for the simplified protein sequence compared with the parent sequence using global sequence alignment. With these reduced alphabets and simplified similarity matrices, we achieve recognition of the protein fold based on the similarity score of the sequence alignment. The coverage in dataset SCOP40 for various levels of reduction on the amino acid types is obtained, which is the number of homologous pairs detected by program BLAST to the number marked by SCOP40. For the reduced alphabets containing 10 types of amino acids, the ability to detect distantly related folds remains almost at the same level as that by the alphabet of 20 types of amino acids, which implies that 10 types of amino acids may be the degree of freedom for characterizing the complexity in proteins.
Shotgun bisulphite sequencing of the arabidopsis genome reveals DNA methylation patterning
- Cokus
Shotgun bisulphite sequencing of the arabidopsis genome reveals DNA methylation patterning
- SJ Cokus
- S Feng
- X Zhang