Clustering Of Protein Sequences Research Articles

Virulence perspective genomic research unlocks the secrets of Rhizoctonia solani associated with banded sheath blight in Barnyard Millet (Echinochloa frumentacea)

IntroductionBanded sheath blight (Bsb) disease, caused by Rhizoctonia solani, is an emerging problem in barnyard millet cultivation. One of the significant goals of pathogenomic research is to identify genes responsible for pathogenicity in the fungus.MethodsA virulence profiling-based approach was employed and six R. solani isolates were collected from various ecological zones of India. The morphological parameters and virulence of all of the six R. solani isolates were investigated. The most virulent strain was designated as RAP2 and its genome has been sequenced, assembled, and annotated.ResultsThe RAP2 genome is 43.63 megabases in size and comprises 10.95% repetitive DNA, within which 46% are retroelements, 8% are DNA transposons, and 46% are unidentified DNA. The Gene Ontology (GO) annotation of RAP2 proteins revealed that “phosphorylation”, “membrane”, and “ATP binding” have the highest gene enrichment in the “biological process”, “cellular component” and “molecular function” domains, respectively. The genome comprises a majority of secretory proteins in the pectin lyase fold/virulence factor superfamily, which break down plant cell wall polymers to extract saccharides. The RAP2 genome is comparable to R. solani, which infects maize and rice, but it diverges further from soybean in terms of nucleotide-level genetic similarity. Orthologous clustering of RAP2 protein sequences with R. solani infecting maize, rice, and soybean yields 5606 proteins shared across all genomes. GO analysis of 25 proteins specific to the RAP2 genome found enrichment in the ethylene response, which can cause spore germination and infection in host plants.DiscussionInterestingly, a 28-bp deletion in the RAP2 strain’s cutinase domain was discovered in the cutinase protein, which might be important in the infection process, perhaps rendering the enzyme inactive or allowing the pathogen to infect barnyard millet while avoiding host defense. This study sheds light on the genetic makeup of R. solani, allowing researchers to discover critical genes related with pathogenicity as well as potential targets for fungicide development.

NMPFamsDB: a database of novel protein families from microbial metagenomes and metatranscriptomes.

The Novel Metagenome Protein Families Database (NMPFamsDB) is a database of metagenome- and metatranscriptome-derived protein families, whose members have no hits to proteins of reference genomes or Pfam domains. Each protein family is accompanied by multiple sequence alignments, Hidden Markov Models, taxonomic information, ecosystem and geolocation metadata, sequence and structure predictions, as well as 3D structure models predicted with AlphaFold2. In its current version, NMPFamsDB hosts over 100000 protein families, each with at least 100 members. The reported protein families significantly expand (more than double) the number of known protein sequence clusters from reference genomes and reveal new insights into their habitat distribution, origins, functions and taxonomy. We expect NMPFamsDB to be a valuable resource for microbial proteome-wide analyses and for further discovery and characterization of novel functions. NMPFamsDB is publicly available in http://www.nmpfamsdb.org/ or https://bib.fleming.gr/NMPFamsDB.

In silico Analysis of Molecular Phylogeny of Genes Involved in the Synthesis of Bioactive Compounds in Cyanobacteria Strains Located in Tehran Cascade

Background: Cyanobacteria is an excellent candidate for discovering bioactive compounds with applications in pharmaceutical industries. Nowadays, the major bioactive metabolites isolated from cyanobacteria are polymerized, nonribosomal peptides (NRPS), or hybrids of both. Despite numerous studies on the gene distribution of bioactive compounds, no studies have been conducted on the cyanobacteria strains found in the Tehran cascade. Objectives: This monitoring study aimed to study the secondary structure of the protein and molecular phylogeny of polypeptides synthase genes and nonribosomal peptides along with the 16S rRNA gene. Methods: In this study, 20 strains of cyanobacteria found in the Tehran Cascade were identified based on a 16rRNA gene sequence. The PCR of the nonribosomal peptides (NRPS) and polyketide synthase (PKS) genes was performed using molecular techniques to analyze the phylogenetic genes responsible for secondary metabolite production. Bioinformatics software was used to predict the composition of peptides, activated amino acids, and signature sequences of the NRPS adenylation module. Finally, the strains studied in the Alborz Herbarium Microbial Culture Collection (CCC) were kept at the Azad Islamic University, Science and Research Unit. Results: The NRPS and PKS genes were found in some strains. Furthermore, the bioinformatics analyses revealed the type of natural compound, the signature sequences, and the predication of the activated amino acid. Clustering of NRPS and PKS protein sequences showed no clear phylogenetic correlation between adenylation domains and the type of activated amino acid, indicating wide diversity within adenylation domains. Conclusions: The biological analysis of polypeptide synthase and nonribosomal peptide synthetases genes may help estimate species that produce natural products and the possible role of these enzymatic complexes in the bio-synthesis of bioactive compounds in the pharmaceutical industry.

Complet+: a computationally scalable method to improve completeness of large-scale protein sequence clustering.

A major challenge for clustering algorithms is to balance the trade-off between homogeneity, i.e., the degree to which an individual cluster includes only related sequences, and completeness, the degree to which related sequences are broken up into multiple clusters. Most algorithms are conservative in grouping sequences with other sequences. Remote homologs may fail to be clustered together and instead form unnecessarily distinct clusters. The resulting clusters have high homogeneity but completeness that is too low. We propose Complet+, a computationally scalable post-processing method to increase the completeness of clusters without an undue cost in homogeneity. Complet+ proves to effectively merge closely-related clusters of protein that have verified structural relationships in the SCOPe classification scheme, improving the completeness of clustering results at little cost to homogeneity. Applying Complet+ to clusters obtained using MMseqs2's clusterupdate achieves an increased V-measure of 0.09 and 0.05 at the SCOPe superfamily and family levels, respectively. Complet+ also creates more biologically representative clusters, as shown by a substantial increase in Adjusted Mutual Information (AMI) and Adjusted Rand Index (ARI) metrics when comparing predicted clusters to biological classifications. Complet+ similarly improves clustering metrics when applied to other methods, such as CD-HIT and linclust. Finally, we show that Complet+ runtime scales linearly with respect to the number of clusters being post-processed on a COG dataset of over 3 million sequences. Code and supplementary information is available on Github: https://github.com/EESI/Complet-Plus.

Classification of Protein Sequences by a Novel Alignment-Free Method on Bacterial and Virus Families.

The classification of protein sequences provides valuable insights into bioinformatics. Most existing methods are based on sequence alignment algorithms, which become time-consuming as the size of the database increases. Therefore, there is a need to develop an improved method for effectively classifying protein sequences. In this paper, we propose a novel accumulated natural vector method to cluster protein sequences at a lower time cost without reducing accuracy. Our method projects each protein sequence as a point in a 250-dimensional space according to its amino acid distribution. Thus, the biological distance between any two proteins can be easily measured by the Euclidean distance between the corresponding points in the 250-dimensional space. The convex hull analysis and classification perform robustly on virus and bacteria datasets, effectively verifying our method.

Organizing the bacterial annotation space with amino acid sequence embeddings

BackgroundDue to the ever-expanding gap between the number of proteins being discovered and their functional characterization, protein function inference remains a fundamental challenge in computational biology. Currently, known protein annotations are organized in human-curated ontologies, however, all possible protein functions may not be organized accurately. Meanwhile, recent advancements in natural language processing and machine learning have developed models which embed amino acid sequences as vectors in n-dimensional space. So far, these embeddings have primarily been used to classify protein sequences using manually constructed protein classification schemes.ResultsIn this work, we describe the use of amino acid sequence embeddings as a systematic framework for studying protein ontologies. Using a sequence embedding, we show that the bacterial carbohydrate metabolism class within the SEED annotation system contains 48 clusters of embedded sequences despite this class containing 29 functional labels. Furthermore, by embedding Bacillus amino acid sequences with unknown functions, we show that these unknown sequences form clusters that are likely to have similar biological roles.ConclusionsThis study demonstrates that amino acid sequence embeddings may be a powerful tool for developing more robust ontologies for annotating protein sequence data. In addition, embeddings may be beneficial for clustering protein sequences with unknown functions and selecting optimal candidate proteins to characterize experimentally.

Performance Analysis of Fuzzy C-means (FCM) and Possibilistic C-means Algorithm (PCM) for Protein Sequence Clustering

Protein sequence clustering is very vast and challenging since number of protein sequences are increasing and their unpredicted protein structures. Once we will be able to cluster the protein sequence which is similar to each other we can easily find their structure. Protein sequences with their physico-chemical properties had been used in this paper. In this paper soft partitioning clustering algorithm are applied i.e. Fuzzy C-Means (FCM) and Possibilistic C-Means (PCM). Thirteen properties had been used to analyze the performance of fuzzy c-means and possibilistic c-means algorithm. Four properties (Pi, Hydropathy, Solubility and Charge) with their experiment results had been discussed in detail. Performance of Fuzzy C-means and Possibilistic C-means algorithm had been evaluated based on Partition Entropy (PE), Partition Coefficient (PC), Modified Partition Coefficient (MPC) and Fuzzy Silhouette Index (SIL.F) metrics using R Programming. The results show that the PCM performs better than FCM.

Density Peak clustering of protein sequences associated to a Pfam clan reveals clear similarities and interesting differences with respect to manual family annotation

BackgroundThe identification of protein families is of outstanding practical importance for in silico protein annotation and is at the basis of several bioinformatic resources. Pfam is possibly the most well known protein family database, built in many years of work by domain experts with extensive use of manual curation. This approach is generally very accurate, but it is quite time consuming and it may suffer from a bias generated from the hand-curation itself, which is often guided by the available experimental evidence.ResultsWe introduce a procedure that aims to identify automatically putative protein families. The procedure is based on Density Peak Clustering and uses as input only local pairwise alignments between protein sequences. In the experiment we present here, we ran the algorithm on about 4000 full-length proteins with at least one domain classified by Pfam as belonging to the Pseudouridine synthase and Archaeosine transglycosylase (PUA) clan. We obtained 71 automatically-generated sequence clusters with at least 100 members. While our clusters were largely consistent with the Pfam classification, showing good overlap with either single or multi-domain Pfam family architectures, we also observed some inconsistencies. The latter were inspected using structural and sequence based evidence, which suggested that the automatic classification captured evolutionary signals reflecting non-trivial features of protein family architectures. Based on this analysis we identified a putative novel pre-PUA domain as well as alternative boundaries for a few PUA or PUA-associated families. As a first indication that our approach was unlikely to be clan-specific, we performed the same analysis on the P53 clan, obtaining comparable results.ConclusionsThe clustering procedure described in this work takes advantage of the information contained in a large set of pairwise alignments and successfully identifies a set of putative families and family architectures in an unsupervised manner. Comparison with the Pfam classification highlights significant overlap and points to interesting differences, suggesting that our new algorithm could have potential in applications related to automatic protein classification. Testing this hypothesis, however, will require further experiments on large and diverse sequence datasets.

Expansion of the Transporter-Opsin-G protein-coupled receptor superfamily with five new protein families.

Here we provide bioinformatic evidence that the Organo-Arsenical Exporter (ArsP), Endoplasmic Reticulum Retention Receptor (KDELR), Mitochondrial Pyruvate Carrier (MPC), L-Alanine Exporter (AlaE), and the Lipid-linked Sugar Translocase (LST) protein families are members of the Transporter-Opsin-G Protein-coupled Receptor (TOG) Superfamily. These families share domains homologous to well-established TOG superfamily members, and their topologies of transmembranal segments (TMSs) are compatible with the basic 4-TMS repeat unit characteristic of this Superfamily. These repeat units tend to occur twice in proteins as a result of intragenic duplication events, often with subsequent gain/loss of TMSs in many superfamily members. Transporters within the ArsP family allow microbial pathogens to expel toxic arsenic compounds from the cell. Members of the KDELR family are involved in the selective retrieval of proteins that reside in the endoplasmic reticulum. Proteins of the MPC family are involved in the transport of pyruvate into mitochondria, providing the organelle with a major oxidative fuel. Members of family AlaE excrete L-alanine from the cell. Members of the LST family are involved in the translocation of lipid-linked glucose across the membrane. These five families substantially expand the range of substrates of transport carriers in the superfamily, although KDEL receptors have no known transport function. Clustering of protein sequences reveals the relationships among families, and the resulting tree correlates well with the degrees of sequence similarity documented between families. The analyses and programs developed to detect distant relatedness, provide insights into the structural, functional, and evolutionary relationships that exist between families of the TOG superfamily, and should be of value to many other investigators.

Hit Dexter 2.0: Machine-Learning Models for the Prediction of Frequent Hitters.

Assay interference caused by small molecules continues to pose a significant challenge for early drug discovery. A number of rule-based and similarity-based approaches have been derived that allow the flagging of potentially "badly behaving compounds", "bad actors", or "nuisance compounds". These compounds are typically aggregators, reactive compounds, and/or pan-assay interference compounds (PAINS), and many of them are frequent hitters. Hit Dexter is a recently introduced machine learning approach that predicts frequent hitters independent of the underlying physicochemical mechanisms (including also the binding of compounds based on "privileged scaffolds" to multiple binding sites). Here we report on the development of a second generation of machine learning models which now covers both primary screening assays and confirmatory dose-response assays. Protein sequence clustering was newly introduced to minimize the overrepresentation of structurally and functionally related proteins. The models correctly classified compounds of large independent test sets as (highly) promiscuous or nonpromiscuous with Matthews correlation coefficient (MCC) values of up to 0.64 and area under the receiver operating characteristic curve (AUC) values of up to 0.96. The models were also utilized to characterize sets of compounds with specific biological and physicochemical properties, such as dark chemical matter, aggregators, compounds from a high-throughput screening library, drug-like compounds, approved drugs, potential PAINS, and natural products. Among the most interesting outcomes is that the new Hit Dexter models predict the presence of large fractions of (highly) promiscuous compounds among approved drugs. Importantly, predictions of the individual Hit Dexter models are generally in good agreement and consistent with those of Badapple, an established statistical model for the prediction of frequent hitters. The new Hit Dexter 2.0 web service, available at http://hitdexter2.zbh.uni-hamburg.de , not only provides user-friendly access to all machine learning models presented in this work but also to similarity-based methods for the prediction of aggregators and dark chemical matter as well as a comprehensive collection of available rule sets for flagging frequent hitters and compounds including undesired substructures.

Comparative analysis of genome sequences of the conifer tree pathogen, Heterobasidion annosum s.s.

The causal agent of root and butt rot of conifer trees, Heterobasidion annosum, is widespread in boreal forests and economically responsible for annual loss of approximately 50 million euros to forest industries in Finland alone and much more at European level. In order to further understand the pathobiology of this fungus at the genome level, a Finnish isolate of H. annosum sensu stricto (isolate 03012) was sequenced and analyzed with the genome sequences of 23 white-rot and 13 brown-rot fungi. The draft genome assembly of H. annosum has a size of 31.01Mb, containing 11,453 predicted genes. Whole genome alignment showed that 84.38% of H. annosum genome sequences were aligned with those of previously sequenced H. irregulare TC 32-1 counterparts. The result is further supported by the protein sequence clustering analysis which revealed that the two genomes share 6719 out of 8647 clusters. When sequencing reads of H. annosum were aligned against the genome sequences of H. irregulare, six single nucleotide polymorphisms were found in every 1kb, on average. In addition, 98.68% of SNPs were found to be homo-variants, suggesting that the two species have long evolved from different niches. Gene family analysis revealed that most of the white-rot fungi investigated had more gene families involved in lignin degradation or modification, including laccases and peroxidase. Comparative analysis of the two Heterobasidion spp. as well as white-/brown-rot fungi would provide new insights for understanding the pathobiology of the conifer tree pathogen.

KinFin: Software for Taxon-Aware Analysis of Clustered Protein Sequences.

The field of comparative genomics is concerned with the study of similarities and differences between the information encoded in the genomes of organisms. A common approach is to define gene families by clustering protein sequences based on sequence similarity, and analyze protein cluster presence and absence in different species groups as a guide to biology. Due to the high dimensionality of these data, downstream analysis of protein clusters inferred from large numbers of species, or species with many genes, is nontrivial, and few solutions exist for transparent, reproducible, and customizable analyses. We present KinFin, a streamlined software solution capable of integrating data from common file formats and delivering aggregative annotation of protein clusters. KinFin delivers analyses based on systematic taxonomy of the species analyzed, or on user-defined, groupings of taxa, for example, sets based on attributes such as life history traits, organismal phenotypes, or competing phylogenetic hypotheses. Results are reported through graphical and detailed text output files. We illustrate the utility of the KinFin pipeline by addressing questions regarding the biology of filarial nematodes, which include parasites of veterinary and medical importance. We resolve the phylogenetic relationships between the species and explore functional annotation of proteins in clusters in key lineages and between custom taxon sets, identifying gene families of interest. KinFin can easily be integrated into existing comparative genomic workflows, and promotes transparent and reproducible analysis of clustered protein data.

Pan-genome and phylogeny of Bacillus cereus sensu lato

BackgroundBacillus cereus sensu lato (s. l.) is an ecologically diverse bacterial group of medical and agricultural significance. In this study, I use publicly available genomes and novel bioinformatic workflows to characterize the B. cereus s. l. pan-genome and perform the largest phylogenetic and population genetic analyses of this group to date in terms of the number of genes and taxa included. With these fundamental data in hand, I identify genes associated with particular phenotypic traits (i.e., “pan-GWAS” analysis), and quantify the degree to which taxa sharing common attributes are phylogenetically clustered.MethodsA rapid k-mer based approach (Mash) was used to create reduced representations of selected Bacillus genomes, and a fast distance-based phylogenetic analysis of this data (FastME) was performed to determine which species should be included in B. cereus s. l. The complete genomes of eight B. cereus s. l. species were annotated de novo with Prokka, and these annotations were used by Roary to produce the B. cereus s. l. pan-genome. Scoary was used to associate gene presence and absence patterns with various phenotypes. The orthologous protein sequence clusters produced by Roary were filtered and used to build HaMStR databases of gene models that were used in turn to construct phylogenetic data matrices. Phylogenetic analyses used RAxML, DendroPy, ClonalFrameML, PAUP*, and SplitsTree. Bayesian model-based population genetic analysis assigned taxa to clusters using hierBAPS. The genealogical sorting index was used to quantify the phylogenetic clustering of taxa sharing common attributes.ResultsThe B. cereus s. l. pan-genome currently consists of ≈60,000 genes, ≈600 of which are “core” (common to at least 99% of taxa sampled). Pan-GWAS analysis revealed genes associated with phenotypes such as isolation source, oxygen requirement, and ability to cause diseases such as anthrax or food poisoning. Extensive phylogenetic analyses using an unprecedented amount of data produced phylogenies that were largely concordant with each other and with previous studies. Phylogenetic support as measured by bootstrap probabilities increased markedly when all suitable pan-genome data was included in phylogenetic analyses, as opposed to when only core genes were used. Bayesian population genetic analysis recommended subdividing the three major clades of B. cereus s. l. into nine clusters. Taxa sharing common traits and species designations exhibited varying degrees of phylogenetic clustering.ConclusionsAll phylogenetic analyses recapitulated two previously used classification systems, and taxa were consistently assigned to the same major clade and group. By including accessory genes from the pan-genome in the phylogenetic analyses, I produced an exceptionally well-supported phylogeny of 114 complete B. cereus s. l. genomes. The best-performing methods were used to produce a phylogeny of all 498 publicly available B. cereus s. l. genomes, which was in turn used to compare three different classification systems and to test the monophyly status of various B. cereus s. l. species. The majority of the methodology used in this study is generic and could be leveraged to produce pan-genome estimates and similarly robust phylogenetic hypotheses for other bacterial groups.

A novel alignment-free vector method to cluster protein sequences

Classification of protein are crucial topics in biology. The number of protein sequences stored in databases increases sharply in the past decade. Traditionally, comparison of protein sequences is usually carried out through multiple sequence alignment methods. However, these methods may be unsuitable for clustering of protein sequences when gene rearrangements occur such as in viral genomes. The computation is also very time-consuming for large datasets with long genomes. In this paper, based on three important biochemical properties of amino acids: the hydropathy index, polar requirement and chemical composition of the side chain, we propose a 24 dimensional feature vector describing the composition of amino acids in protein sequences. Our method not only utilizes the chemical properties of amino acids but also counts on their numbers and positions. The results on beta-globin, mammals, and three virus datasets show that this new tool is fast and accurate for classifying proteins and inferring the phylogeny of organisms.

How to Cluster Protein Sequences: Tools, Tips and Commands

How to Cluster Protein Sequences: Tools, Tips and Commands

A novel alignment-free method to classify protein folding types by combining spectral graph clustering with Chou's pseudo amino acid composition

The present work employs pseudo amino acid composition (PseAAC) for encoding the protein sequences in their numeric form. Later this will be arranged in the similarity matrix, which serves as input for spectral graph clustering method. Spectral methods are used previously also for clustering of protein sequences, but they uses pair wise alignment scores of protein sequences, in similarity matrix. The alignment score depends on the length of sequences, so clustering short and long sequences together may not good idea. Therefore the idea of introducing PseAAC with spectral clustering algorithm came into scene. We extensively tested our method and compared its performance with other existing machine learning methods. It is consistently observed that, the number of clusters that we obtained for a given set of proteins is close to the number of superfamilies in that set and PseAAC combined with spectral graph clustering shows the best classification results.

An approach to functionally relevant clustering of the protein universe: Active site profile-based clustering of protein structures and sequences.

Protein function identification remains a significant problem. Solving this problem at the molecular functional level would allow mechanistic determinant identification—amino acids that distinguish details between functional families within a superfamily. Active site profiling was developed to identify mechanistic determinants. DASP and DASP2 were developed as tools to search sequence databases using active site profiling. Here, TuLIP (Two‐Level Iterative clustering Process) is introduced as an iterative, divisive clustering process that utilizes active site profiling to separate structurally characterized superfamily members into functionally relevant clusters. Underlying TuLIP is the observation that functionally relevant families (curated by Structure‐Function Linkage Database, SFLD) self‐identify in DASP2 searches; clusters containing multiple functional families do not. Each TuLIP iteration produces candidate clusters, each evaluated to determine if it self‐identifies using DASP2. If so, it is deemed a functionally relevant group. Divisive clustering continues until each structure is either a functionally relevant group member or a singlet. TuLIP is validated on enolase and glutathione transferase structures, superfamilies well‐curated by SFLD. Correlation is strong; small numbers of structures prevent statistically significant analysis. TuLIP‐identified enolase clusters are used in DASP2 GenBank searches to identify sequences sharing functional site features. Analysis shows a true positive rate of 96%, false negative rate of 4%, and maximum false positive rate of 4%. F‐measure and performance analysis on the enolase search results and comparison to GEMMA and SCI‐PHY demonstrate that TuLIP avoids the over‐division problem of these methods. Mechanistic determinants for enolase families are evaluated and shown to correlate well with literature results.

Large-scale modular comparative genomics: the Grid approach

Modes Of Operation The 4 modes of operation are presented below: 1. MCL clusters of the protein query and database Results In the era of Big Data in Life Sciences, efficient process and analysis of vast amounts of sequence We have implemented the proposed framework using a number of scripts suitable for a Unix sequences. The clustering criteria is the BLAST output (identity or e-value), based on the preference of the user 2. Phylogenetic profiles of each query sequence, where the genomes into consideration are the ones whose proteins form the database 3. MCL clusters of the protein query sequences and database genomes, and phylogenetic profiles. The MCL clustering criteria is the phylogenetic profiles. 4. This mode is essentially a combination of the data is becoming an ever daunting challenge. Among such analyses, sequence alignment is one of the most commonly used procedures, as it provides useful insights on the functionality and relationship of the involved entities. At the same time however, it is one of the most common computational bottlenecks in several bioinformatics workflows, especially when combined with the construction of families and phylogenetic profiles. We have designed and implemented a time-efficient distributed modular application for sequence environment of a Grid Infrastructure. We present the scaling of the average job execution time and inqueue time, for different number of submitted jobs. The in-queue time is computed as the time spent from the job submission until the time that its execution was initiated. Program Flow output produced in modes 1 and 3. There is also a fifth mode that generates the same output as the fourth one, with the only difference being that the same file is used both as a database and a query. This is the case of an all-vs-all sequence comparison, widely used when performing a pangenome analysis. alignment, phylogenetic profiling and clustering of protein sequences, by utilizing the European Grid Infrastructure. Specifically, the application comprises three main components: (a) BLAST alignment, (b) construction of phylogenetic profiles based on the produced alignment scores, and (c) clustering of entities using the MCL algorithm. These modules have been selected as they represent a common aspect of a vast majority of Fig. 4: Average run and in-queue time per job, as a function of the number of jobs submitted Furthermore, we have evaluated the framework on a real-world scenario, i.e. the analysis of a plant pangenome bioinformatics workflows. It is important to note that the modules can be combined independently, and ultimately provide 4 different modes of operation. We have evaluated the application through several different scenarios, ranging from targeted investigations of enzymes participating in selected pathways against a custom database to produce functional groups, to large scale comparisons at the pangenome level. In all cases, the optimal utilization of the Grid with regards to the respective modules, Our proposed framework proceeds with the distribution of both processes and data across the provided resources. The distribution is performed automatically, based on the selected mode as well as the data under study. . References Background allowed us to achieve significant speedup, in the order of 14x with respect to traditional approaches. Grid Computing is an established method of high performance computing that is mostly utilized by embarrassingly parallel processes. Duarte AMS, Psomopoulos FE et. al. (2015): Future opportunities Fig. 5: Output of the All-vs-All comparison of the ~2M sequences of the plant pangenome . Horizontal axis: 98 plant species, Vertical axis: 1.979.749 phylogenetic profiles. Overall execution time with the proposed framework: ~300 h, avg. phylogenetic profile time:16.21 h, avg. BLAST time: 28.05 h, avg. time-in-queue: 66.1 h, MCL: 28 h Useful resources The input comprises of the following files: 1. two files containing the query protein sequences and the database protein sequences to be aligned, in FASTA format, which is a text-based format for representing nucleotide or peptide sequences, and trends for e-infrastructures and life sciences: going beyond the grid to enable life science data analysis. Front. Genet. 6:197 A.J. Enright, S. Van Dongen, C. A. Ouzounis (2002): An efficient algorithm for large-scale detection of protein families. Nucleic Acids Res.; 30(7): 1575–1584 Psomopoulos, Fotis E, et. al. (2014): The Chlamydiales Pangenome Revisited: Structural Stability and Functional Coherence, Genes 3(2): 291-319 Fig. 2: General flow chart of the process Fig. 1: An example of a Grid Architecture Source code: https://github.com/BioDAG/BPM European Grid Infrastructure (http://www.egi.eu/)

Evaluation of BLAST-based edge-weighting metrics used for homology inference with the Markov Clustering algorithm.

BackgroundClustering protein sequences according to inferred homology is a fundamental step in the analysis of many large data sets. Since the publication of the Markov Clustering (MCL) algorithm in 2002, it has been the centerpiece of several popular applications. Each of these approaches generates an undirected graph that represents sequences as nodes connected to each other by edges weighted with a BLAST-based metric. MCL is then used to infer clusters of homologous proteins by analyzing these graphs. The various approaches differ only by how they weight the edges, yet there has been very little direct examination of the relative performance of alternative edge-weighting metrics. This study compares the performance of four BLAST-based edge-weighting metrics: the bit score, bit score ratio (BSR), bit score over anchored length (BAL), and negative common log of the expectation value (NLE). Performance is tested using the Extended CEGMA KOGs (ECK) database, which we introduce here.ResultsAll metrics performed similarly when analyzing full-length sequences, but dramatic differences emerged as progressively larger fractions of the test sequences were split into fragments. The BSR and BAL successfully rescued subsets of clusters by strengthening certain types of alignments between fragmented sequences, but also shifted the largest correct scores down near the range of scores generated from spurious alignments. This penalty outweighed the benefits in most test cases, and was greatly exacerbated by increasing the MCL inflation parameter, making these metrics less robust than the bit score or the more popular NLE. Notably, the bit score performed as well or better than the other three metrics in all scenarios.ConclusionsThe results provide a strong case for use of the bit score, which appears to offer equivalent or superior performance to the more popular NLE. The insight that MCL-based clustering methods can be improved using a more tractable edge-weighting metric will greatly simplify future implementations. We demonstrate this with our own minimalist Python implementation: Porthos, which uses only standard libraries and can process a graph with 25 m + edges connecting the 60 k + KOG sequences in half a minute using less than half a gigabyte of memory.Electronic supplementary materialThe online version of this article (doi:10.1186/s12859-015-0625-x) contains supplementary material, which is available to authorized users.

Parallel Massive Clustering of Discrete Distributions

The trend of analyzing big data in artificial intelligence demands highly-scalable machine learning algorithms, among which clustering is a fundamental and arguably the most widely applied method. To extend the applications of regular vector-based clustering algorithms, the Discrete Distribution (D2) clustering algorithm has been developed, aiming at clustering data represented by bags of weighted vectors which are well adopted data signatures in many emerging information retrieval and multimedia learning applications. However, the high computational complexity of D2-clustering limits its impact in solving massive learning problems. Here we present the parallel D2-clustering (PD2-clustering) algorithm with substantially improved scalability. We developed a hierarchical multipass algorithm structure for parallel computing in order to achieve a balance between the individual-node computation and the integration process of the algorithm. Experiments and extensive comparisons between PD2-clustering and other clustering algorithms are conducted on synthetic datasets. The results show that the proposed parallel algorithm achieves significant speed-up with minor accuracy loss. We apply PD2-clustering to image concept learning. In addition, by extending D2-clustering to symbolic data, we apply PD2-clustering to protein sequence clustering. For both applications, we demonstrate the high competitiveness of our new algorithm in comparison with other state-of-the-art methods.