Ultra-fast sequence clustering from similarity networks with <Emphasis FontCategory="NonProportional">SiLiX</Emphasis>

Background  

The number of gene sequences that are available for comparative genomics approaches is increasing extremely quickly. A current challenge is to be able to handle this huge amount of sequences in order to build families of homologous sequences in a reasonable time.     

Predicting mostly disordered proteins by using structure-unknown protein data

Background  

Predicting intrinsically disordered proteins is important in structural biology because they are thought to carry out various cellular functions even though they have no stable three-dimensional structure. We know the structures of far more ordered proteins than disordered proteins. The structural distribution of proteins in nature can therefore be inferred to differ from that of proteins whose structures have been determined experimentally. We know many more protein sequences than we do protein structures, and many of the known sequences can be expected to be those of disordered proteins. Thus it would be efficient to use the information of structure-unknown proteins in order to avoid training data sparseness. We propose a novel method for predicting which proteins are mostly disordered by using spectral graph transducer and training with a huge amount of structure-unknown sequences as well as structure-known sequences.     

A fast algorithm for determining the best combination of local alignments to a query sequence

Background  

Existing sequence alignment algorithms assume that similarities between DNA or amino acid sequences are linearly ordered. That is, stretches of similar nucleotides or amino acids are in the same order in both sequences. Recombination perturbs this order. An algorithm that can reconstruct sequence similarity despite rearrangement would be helpful for reconstructing the evolutionary history of recombined sequences.     

ENGINES: exploring single nucleotide variation in entire human genomes

Background  

Next generation ultra-sequencing technologies are starting to produce extensive quantities of data from entire human genome or exome sequences, and therefore new software is needed to present and analyse this vast amount of information. The 1000 Genomes project has recently released raw data for 629 complete genomes representing several human populations through their Phase I interim analysis and, although there are certain public tools available that allow exploration of these genomes, to date there is no tool that permits comprehensive population analysis of the variation catalogued by such data.     

AMYPdb: A database dedicated to amyloid precursor proteins

Background  

Misfolding and aggregation of proteins into ordered fibrillar structures is associated with a number of severe pathologies, including Alzheimer's disease, prion diseases, and type II diabetes. The rapid accumulation of knowledge about the sequences and structures of these proteins allows using of in silico methods to investigate the molecular mechanisms of their abnormal conformational changes and assembly. However, such an approach requires the collection of accurate data, which are inconveniently dispersed among several generalist databases.     

Micro-Mar: a database for dynamic representation of marine microbial biodiversity

Background  

The cataloging of marine prokaryotic DNA sequences is a fundamental aspect for bioprospecting and also for the development of evolutionary and speciation models. However, large amount of DNA sequences used to quantify prokaryotic biodiversity requires proper tools for storing, managing and analyzing these data for research purposes.     

MatGAT: An application that generates similarity/identity matrices using protein or DNA sequences

Background  

The rapid increase in the amount of protein and DNA sequence information available has become almost overwhelming to researchers. So much information is now accessible that high-quality, functional gene analysis and categorization has become a major goal for many laboratories. To aid in this categorization, there is a need for non-commercial software that is able to both align sequences and also calculate pairwise levels of similarity/identity.     

On the phylogeny of Mustelidae subfamilies: analysis of seventeen nuclear non-coding loci and mitochondrial complete genomes

Background  

Mustelidae, as the largest and most-diverse family of order Carnivora, comprises eight subfamilies. Phylogenetic relationships among these Mustelidae subfamilies remain argumentative subjects in recent years. One of the main reasons is that the mustelids represent a typical example of rapid evolutionary radiation and recent speciation event. Prior investigation has been concentrated on the application of different mitochondrial (mt) sequence and nuclear protein-coding data, herein we employ 17 nuclear non-coding loci (>15 kb), in conjunction with mt complete genome data (>16 kb), to clarify these enigmatic problems.     

4SALE – A tool for synchronous RNA sequence and secondary structure alignment and editing

Background  

In sequence analysis the multiple alignment builds the fundament of all proceeding analyses. Errors in an alignment could strongly influence all succeeding analyses and therefore could lead to wrong predictions. Hand-crafted and hand-improved alignments are necessary and meanwhile good common practice. For RNA sequences often the primary sequence as well as a secondary structure consensus is well known, e.g., the cloverleaf structure of the t-RNA. Recently, some alignment editors are proposed that are able to include and model both kinds of information. However, with the advent of a large amount of reliable RNA sequences together with their solved secondary structures (available from e.g. the ITS2 Database), we are faced with the problem to handle sequences and their associated secondary structures synchronously.     

The Subviral RNA Database: a toolbox for viroids, the hepatitis delta virus and satellite RNAs research

Background  

Viroids, satellite RNAs, satellites viruses and the human hepatitis delta virus form the 'brotherhood' of the smallest known infectious RNA agents, known as the subviral RNAs. For most of these species, it is generally accepted that characteristics such as cell movement, replication, host specificity and pathogenicity are encoded in their RNA sequences and their resulting RNA structures. Although many sequences are indexed in publicly available databases, these sequence annotation databases do not provide the advanced searches and data manipulation capability for identifying and characterizing subviral RNA motifs.     

Fungal-specific PCR primers developed for analysis of the ITS region of environmental DNA extracts

Background  

The Internal Transcribed Spacer (ITS) regions of fungal ribosomal DNA (rDNA) are highly variable sequences of great importance in distinguishing fungal species by PCR analysis. Previously published PCR primers available for amplifying these sequences from environmental samples provide varying degrees of success at discriminating against plant DNA while maintaining a broad range of compatibility. Typically, it has been necessary to use multiple primer sets to accommodate the range of fungi under study, potentially creating artificial distinctions for fungal sequences that amplify with more than one primer set.     

IdentiCS – Identification of coding sequence and <Emphasis Type="Italic">in silico</Emphasis> reconstruction of the metabolic network directly from unannotated low-coverage bacterial genome sequence

Background  

A necessary step for a genome level analysis of the cellular metabolism is the in silico reconstruction of the metabolic network from genome sequences. The available methods are mainly based on the annotation of genome sequences including two successive steps, the prediction of coding sequences (CDS) and their function assignment. The annotation process takes time. The available methods often encounter difficulties when dealing with unfinished error-containing genomic sequence.     

<Emphasis Type="Italic">Phylo-mLogo</Emphasis>: an interactive and hierarchical multiple-logo visualization tool for alignment of many sequences

Background  

When aligning several hundreds or thousands of sequences, such as epidemic virus sequences or homologous/orthologous sequences of some big gene families, to reconstruct the epidemiological history or their phylogenies, how to analyze and visualize the alignment results of many sequences has become a new challenge for computational biologists. Although there are several tools available for visualization of very long sequence alignments, few of them are applicable to the alignments of many sequences.     

Java GUI for InterProScan (JIPS): A tool to help process multiple InterProScans and perform ortholog analysis

Background  

Recent, rapid growth in the quantity of available genomic data has generated many protein sequences that are not yet biochemically classified. Thus, the prediction of biochemical function based on structural motifs is an important task in post-genomic analysis. The InterPro databases are a major resource for protein function information. For optimal results, these databases should be searched at regular intervals, since they are frequently updated.     

Resolution among major placental mammal interordinal relationships with genome data imply that speciation influenced their earliest radiations

Background  

A number of the deeper divergences in the placental mammal tree are still inconclusively resolved despite extensive phylogenomic analyses. A recent analysis of 200 kbp of protein coding sequences yielded only limited support for the relationships among Laurasiatheria (cow, dog, bat and shrew), probably because the divergences occurred only within a few million years from each other. It is generally expected that increasing the amount of data and improving the taxon sampling enhance the resolution of narrow divergences. Therefore these and other difficult splits were examined by phylogenomic analysis of the hitherto largest sequence alignment. The increasingly complete genome data of placental mammals also allowed developing a novel and stringent data search method.     

Multi-locus analysis of human infective <Emphasis Type="Italic">Cryptosporidium</Emphasis> species and subtypes using ten novel genetic loci

Background  

Cryptosporidium is a protozoan parasite that causes diarrheal illness in a wide range of hosts including humans. Two species, C. parvum and C. hominis are of primary public health relevance. Genome sequences of these two species are available and show only 3-5% sequence divergence. We investigated this sequence variability, which could correspond either to sequence gaps in the published genome sequences or to the presence of species-specific genes. Comparative genomic tools were used to identify putative species-specific genes and a subset of these genes was tested by PCR in a collection of Cryptosporidium clinical isolates and reference strains.     

Analysis and comparison of very large metagenomes with fast clustering and functional annotation

Background  

The remarkable advance of metagenomics presents significant new challenges in data analysis. Metagenomic datasets (metagenomes) are large collections of sequencing reads from anonymous species within particular environments. Computational analyses for very large metagenomes are extremely time-consuming, and there are often many novel sequences in these metagenomes that are not fully utilized. The number of available metagenomes is rapidly increasing, so fast and efficient metagenome comparison methods are in great demand.     

Approaching the taxonomic affiliation of unidentified sequences in public databases – an example from the mycorrhizal fungi

Background  

During the last few years, DNA sequence analysis has become one of the primary means of taxonomic identification of species, particularly so for species that are minute or otherwise lack distinct, readily obtainable morphological characters. Although the number of sequences available for comparison in public databases such as GenBank increases exponentially, only a minuscule fraction of all organisms have been sequenced, leaving taxon sampling a momentous problem for sequence-based taxonomic identification. When querying GenBank with a set of unidentified sequences, a considerable proportion typically lack fully identified matches, forming an ever-mounting pile of sequences that the researcher will have to monitor manually in the hope that new, clarifying sequences have been submitted by other researchers. To alleviate these concerns, a project to automatically monitor select unidentified sequences in GenBank for taxonomic progress through repeated local BLAST searches was initiated. Mycorrhizal fungi – a field where species identification often is prohibitively complex – and the much usedITSlocus were chosen as test bed.