Sensitive Homology Search Research Articles

VOGDB-Database of Virus Orthologous Groups.

Computational models of homologous protein groups are essential in sequence bioinformatics. Due to the diversity and rapid evolution of viruses, the grouping of protein sequences from virus genomes is particularly challenging. The low sequence similarities of homologous genes in viruses require specific approaches for sequence- and structure-based clustering. Furthermore, the annotation of virus genomes in public databases is not as consistent and up to date as for many cellular genomes. To tackle these problems, we have developed VOGDB, which is a database of virus orthologous groups. VOGDB is a multi-layer database that progressively groups viral genes into groups connected by increasingly remote similarity. The first layer is based on pair-wise sequence similarities, the second layer is based on the sequence profile alignments, and the third layer uses predicted protein structures to find the most remote similarity. VOGDB groups allow for more sensitive homology searches of novel genes and increase the chance of predicting annotations or inferring phylogeny. VOGD B uses all virus genomes from RefSeq and partially reannotates them. VOGDB is updated with every RefSeq release. The unique feature of VOGDB is the inclusion of both prokaryotic and eukaryotic viruses in the same clustering process, which makes it possible to explore old evolutionary relationships of the two groups. VOGDB is freely available at vogdb.org under the CC BY 4.0 license.

Lambda3: homology search for protein, nucleotide, and bisulfite-converted sequences.

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. 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. Lambda3 is free and open-source software publicly available at https://github.com/seqan/lambda/.

Tracing the evolution of the plant meiotic molecular machinery

Meiosis is a highly conserved specialised cell division in sexual life cycles of eukaryotes, forming the base of gene reshuffling, biological diversity and evolution. Understanding meiotic machinery across different plant lineages is inevitable to understand the lineage-specific evolution of meiosis. Functional and cytogenetic studies of meiotic proteins from all plant lineage representatives are nearly impossible. So, we took advantage of the genomics revolution to search for core meiotic proteins in accumulating plant genomes by the highly sensitive homology search approaches, PSI-BLAST, HMMER and CLANS. We could find that most of the meiotic proteins are conserved in most of the lineages. Exceptionally, Arabidopsis thaliana ASY4, PHS1, PRD2, PRD3 orthologs were mostly not detected in some distant algal lineages suggesting their minimal conservation. Remarkably, an ancestral duplication of SPO11 to all eukaryotes could be confirmed. Loss of SPO11-1 in Chlorophyta and Charophyta is likely to have occurred, suggesting that SPO11-1 and SPO11-2 heterodimerisation may be a unique feature in land plants of Viridiplantae. The possible origin of the meiotic proteins described only in plants till now, DFO and HEIP1, could be traced and seems to occur in the ancestor of vascular plants and Streptophyta, respectively. Our comprehensive approach is an attempt to provide insights about meiotic core proteins and thus the conservation of meiotic pathways across plant kingdom. We hope that this will serve the meiotic community a basis for further characterisation of interesting candidates in future.

A family of unusual immunoglobulin superfamily genes in an invertebrate histocompatibility complex

Most colonial marine invertebrates are capable of allorecognition, the ability to distinguish between themselves and conspecifics. One long-standing question is whether invertebrate allorecognition genes are homologous to vertebrate histocompatibility genes. In the cnidarian Hydractinia symbiolongicarpus, allorecognition is controlled by at least two genes, Allorecognition 1 (Alr1) and Allorecognition 2 (Alr2), which encode highly polymorphic cell-surface proteins that serve as markers of self. Here, we show that Alr1 and Alr2 are part of a family of 41 Alr genes, all of which reside in a single genomic interval called the Allorecognition Complex (ARC). Using sensitive homology searches and highly accurate structural predictions, we demonstrate that the Alr proteins are members of the immunoglobulin superfamily (IgSF) with V-set and I-set Ig domains unlike any previously identified in animals. Specifically, their primary amino acid sequences lack many of the motifs considered diagnostic for V-set and I-set domains, yet they adopt secondary and tertiary structures nearly identical to canonical Ig domains. Thus, the V-set domain, which played a central role in the evolution of vertebrate adaptive immunity, was present in the last common ancestor of cnidarians and bilaterians. Unexpectedly, several Alr proteins also have immunoreceptor tyrosine-based activation motifs and immunoreceptor tyrosine-based inhibitory motifs in their cytoplasmic tails, suggesting they could participate in pathways homologous to those that regulate immunity in humans and flies. This work expands our definition of the IgSF with the addition of a family of unusual members, several of which play a role in invertebrate histocompatibility.

Computational Structural Genomics Unravels Common Folds and Novel Families in the Secretome of Fungal Phytopathogen Magnaporthe oryzae.

Structural biology has the potential to illuminate the evolution of pathogen effectors and their commonalities that cannot be readily detected at the primary sequence level. Recent breakthroughs in protein structure modeling have demonstrated the feasibility to predict the protein folds without depending on homologous templates. These advances enabled a genome-wide computational structural biology approach to help understand proteins based on their predicted folds. In this study, we employed structure prediction methods on the secretome of the destructive fungal pathogen Magnaporthe oryzae. Out of 1,854 secreted proteins, we predicted the folds of 1,295 proteins (70%). We showed that template-free modeling by TrRosetta captured 514 folds missed by homology modeling, including many known effectors and virulence factors, and that TrRosetta generally produced higher quality models for secreted proteins. Along with sensitive homology search, we employed structure-based clustering, defining not only homologous groups with divergent members but also sequence-unrelated structurally analogous groups. We demonstrate that this approach can reveal new putative members of structurally similar MAX effectors and novel analogous effector families present in M. oryzae and possibly in other phytopathogens. We also investigated the evolution of expanded putative ADP-ribose transferases with predicted structures. We suggest that the loss of catalytic activities of the enzymes might have led them to new evolutionary trajectories to be specialized as protein binders. Collectively, we propose that computational structural genomics approaches can be an integral part of studying effector biology and provide valuable resources that were inaccessible before the advent of machine learning-based structure prediction.

COMER2: GPU-accelerated sensitive and specific homology searches

SummarySearching for homology in the vast amount of sequence data has a particular emphasis on its speed. We present a completely rewritten version of the sensitive homology search method COMER based on alignment of protein sequence profiles, which is capable of searching big databases even on a lightweight laptop. By harnessing the power of CUDA-enabled graphics processing units, it is up to 20 times faster than HHsearch, a state-of-the-art method using vectorized instructions on modern CPUs.Availability and implementationCOMER2 is cross-platform open-source software available at https://sourceforge.net/projects/comer2 and https://github.com/minmarg/comer2. It can be easily installed from source code or using stand-alone installers.Contact mindaugas.margelevicius@bti.vu.lt Supplementary information Supplementary data are available at Bioinformatics online.

Improve homology search sensitivity of PacBio data by correcting frameshifts.

Single-molecule, real-time sequencing (SMRT) developed by Pacific BioSciences produces longer reads than secondary generation sequencing technologies such as Illumina. The long read length enables PacBio sequencing to close gaps in genome assembly, reveal structural variations, and identify gene isoforms with higher accuracy in transcriptomic sequencing. However, PacBio data has high sequencing error rate and most of the errors are insertion or deletion errors. During alignment-based homology search, insertion or deletion errors in genes will cause frameshifts and may only lead to marginal alignment scores and short alignments. As a result, it is hard to distinguish true alignments from random alignments and the ambiguity will incur errors in structural and functional annotation. Existing frameshift correction tools are designed for data with much lower error rate and are not optimized for PacBio data. As an increasing number of groups are using SMRT, there is an urgent need for dedicated homology search tools for PacBio data. In this work, we introduce Frame-Pro, a profile homology search tool for PacBio reads. Our tool corrects sequencing errors and also outputs the profile alignments of the corrected sequences against characterized protein families. We applied our tool to both simulated and real PacBio data. The results showed that our method enables more sensitive homology search, especially for PacBio data sets of low sequencing coverage. In addition, we can correct more errors when comparing with a popular error correction tool that does not rely on hybrid sequencing. The source code is freely available at https://sourceforge.net/projects/frame-pro/ yannisun@msu.edu.

Rapid similarity search of proteins using alignments of domain arrangements

Homology search methods are dominated by the central paradigm that sequence similarity is a proxy for common ancestry and, by extension, functional similarity. For determining sequence similarity in proteins, most widely used methods use models of sequence evolution and compare amino-acid strings in search for conserved linear stretches. Probabilistic models or sequence profiles capture the position-specific variation in an alignment of homologous sequences and can identify conserved motifs or domains. While profile-based search methods are generally more accurate than simple sequence comparison methods, they tend to be computationally more demanding. In recent years, several methods have emerged that perform protein similarity searches based on domain composition. However, few methods have considered the linear arrangements of domains when conducting similarity searches, despite strong evidence that domain order can harbour considerable functional and evolutionary signal. Here, we introduce an alignment scheme that uses a classical dynamic programming approach to the global alignment of domains. We illustrate that representing proteins as strings of domains (domain arrangements) and comparing these strings globally allows for a both fast and sensitive homology search. Further, we demonstrate that the presented methods complement existing methods by finding similar proteins missed by popular amino-acid-based comparison methods. An implementation of the presented algorithms, a web-based interface as well as a command-line program for batch searching against the UniProt database can be found at http://rads.uni-muenster.de. Furthermore, we provide a JAVA API for programmatic access to domain-string–based search methods.

The product of C9orf72, a gene strongly implicated in neurodegeneration, is structurally related to DENN Rab-GEFs.

Motivation: Fronto-temporal dementia (FTD) and amyotrophic lateral sclerosis (ALS, also called motor neuron disease, MND) are severe neurodegenerative diseases that show considerable overlap at the clinical and cellular level. The most common single mutation in families with FTD or ALS has recently been mapped to a non-coding repeat expansion in the uncharacterized gene C9ORF72. Although a plausible mechanism for disease is that aberrant C9ORF72 mRNA poisons splicing, it is important to determine the cellular function of C9ORF72, about which nothing is known.Results: Sensitive homology searches showed that C9ORF72 is a full-length distant homologue of proteins related to Differentially Expressed in Normal and Neoplasia (DENN), which is a GDP/GTP exchange factor (GEF) that activates Rab-GTPases. Our results suggest that C9ORF72 is likely to regulate membrane traffic in conjunction with Rab-GTPase switches, and we propose to name the gene and its product DENN-like 72 (DENNL72).Supplementary information: Supplementary data are available at Bioinformatics online.Contact: tim.levine@ucl.ac.uk

GHOSTM: A GPU-Accelerated Homology Search Tool for Metagenomics

BackgroundA large number of sensitive homology searches are required for mapping DNA sequence fragments to known protein sequences in public and private databases during metagenomic analysis. BLAST is currently used for this purpose, but its calculation speed is insufficient, especially for analyzing the large quantities of sequence data obtained from a next-generation sequencer. However, faster search tools, such as BLAT, do not have sufficient search sensitivity for metagenomic analysis. Thus, a sensitive and efficient homology search tool is in high demand for this type of analysis.Methodology/Principal FindingsWe developed a new, highly efficient homology search algorithm suitable for graphics processing unit (GPU) calculations that was implemented as a GPU system that we called GHOSTM. The system first searches for candidate alignment positions for a sequence from the database using pre-calculated indexes and then calculates local alignments around the candidate positions before calculating alignment scores. We implemented both of these processes on GPUs. The system achieved calculation speeds that were 130 and 407 times faster than BLAST with 1 GPU and 4 GPUs, respectively. The system also showed higher search sensitivity and had a calculation speed that was 4 and 15 times faster than BLAT with 1 GPU and 4 GPUs.ConclusionsWe developed a GPU-optimized algorithm to perform sensitive sequence homology searches and implemented the system as GHOSTM. Currently, sequencing technology continues to improve, and sequencers are increasingly producing larger and larger quantities of data. This explosion of sequence data makes computational analysis with contemporary tools more difficult. We developed GHOSTM, which is a cost-efficient tool, and offer this tool as a potential solution to this problem.

SiDirect 2.0: updated software for designing functional siRNA with reduced seed-dependent off-target effect

BackgroundRNA interference (RNAi), mediated by 21-nucleotide (nt)-length small interfering RNAs (siRNAs), is a powerful tool not only for studying gene function but also for therapeutic applications. RNAi, requiring perfect complementarity between the siRNA guide strand and the target mRNA, was believed to be extremely specific. However, a recent growing body of evidence has suggested that siRNA could down-regulate unintended genes whose transcripts possess complementarity to the 7-nt siRNA seed region. This off-target gene silencing may often provide incongruous results obtained from knockdown experiments, leading to misinterpretation. Thus, an efficient algorithm for designing functional siRNAs with minimal off-target effect based on the mechanistic features is considered of value.ResultsWe present siDirect 2.0, an update of our web-based software siDirect, which provides functional and off-target minimized siRNA design for mammalian RNAi. The previous version of our software designed functional siRNAs by considering the relationship between siRNA sequence and RNAi activity, and provided them along with the enumeration of potential off-target gene candidates by using a fast and sensitive homology search algorithm. In the new version, the siRNA design algorithm is extensively updated to eliminate off-target effects by reflecting our recent finding that the capability of siRNA to induce off-target effect is highly correlated to the thermodynamic stability, or the melting temperature (Tm), of the seed-target duplex, which is formed between the nucleotides positioned at 2-8 from the 5' end of the siRNA guide strand and its target mRNA. Selection of siRNAs with lower seed-target duplex stabilities (benchmark Tm < 21.5°C) followed by the elimination of unrelated transcripts with nearly perfect match should minimize the off-target effects.ConclusionsiDirect 2.0 provides functional, target-specific siRNA design with the updated algorithm which significantly reduces off-target silencing. When the candidate functional siRNAs could form seed-target duplexes with Tm values below 21.5°C, and their 19-nt regions spanning positions 2-20 of both strands have at least two mismatches to any other non-targeted transcripts, siDirect 2.0 can design at least one qualified siRNA for >94% of human mRNA sequences in RefSeq. siDirect 2.0 is available at http://siDirect2.RNAi.jp/.

PatternHunter: faster and more sensitive homology search

Genomics and proteomics studies routinely depend on homology searches based on the strategy of finding short seed matches which are then extended. The exploding genomic data growth presents a dilemma for DNA homology search techniques: increasing seed size decreases sensitivity whereas decreasing seed size slows down computation. We present a new homology search algorithm 'PatternHunter' that uses a novel seed model for increased sensitivity and new hit-processing techniques for significantly increased speed. At Blast levels of sensitivity, PatternHunter is able to find homologies between sequences as large as human chromosomes, in mere hours on a desktop. PatternHunter is available at http://www.bioinformaticssolutions.com, as a commercial package. It runs on all platforms that support Java. PatternHunter technology is being patented; commercial use requires a license from BSI, while non-commercial use will be free.

A human protein containing multiple types of protease-inhibitory modules.

By using sensitive homology-search and gene-finding programs, we have found that a genomic region from the tip of the short arm of human chromosome 16 (16p13.3) encodes a putative secreted protein consisting of a domain related to the whey acidic protein (WAP) domain, a domain homologous with follistatin modules of the Kazal-domain family (FS module), an immunoglobulin-related domain (Ig domain), two tandem domains related to Kunitz-type protease inhibitor modules (KU domains), and a domain belonging to the recently defined NTR-module family (NTR domain). The gene encoding these WAP, FS, Ig, KU, and NTR modules (hereafter referred to as the WFIKKN gene) is intron-depleted--its single 1,157-bp intron splits the WAP module. The validity of our gene prediction was confirmed by sequencing a WFIKKN cDNA cloned from a lung cDNA library. Studies on the tissue-expression pattern of the WFIKKN gene have shown that the gene is expressed primarily in pancreas, kidney, liver, placenta, and lung. As to the function of the WFIKKN protein, it is noteworthy that it contains FS, WAP, and KU modules, i.e., three different module types homologous with domains frequently involved in inhibition of serine proteases. The protein also contains an NTR module, a domain type implicated in inhibition of zinc metalloproteinases of the metzincin family. On the basis of its intriguing homologies, we suggest that the WFIKKN protein is a multivalent protease inhibitor that may control the action of multiple types of serine proteases as well as metalloproteinase(s).

Highly Sensitive Homology Search Methods on Parallel Computer

Highly Sensitive Homology Search Methods on Parallel Computer

Oligopeptide biases in protein sequences and their use in predicting protein coding regions in nucleotide sequences.

We have examined oligopeptides with lengths ranging from 2 to 11 residues in protein sequences that show no obvious evolutionary relationship. All sequences in the Protein Identification Resource database were carefully classified by sensitive homology searches into superfamilies to obtain unbiased oligopeptide counts. The results, contrary to previous studies, show clear prejudices in protein sequences. The oligopeptide preferences were used to help decide the significance of sequence homologies and to improve the more general methods for detecting protein coding regions within nucleotide sequences.