Multiple Related Sequences Research Articles

RNAdetect: efficient computational detection of novel non-coding RNAs.

Non-coding RNAs (ncRNAs) are known to play crucial roles in various biological processes, and there is a pressing need for accurate computational detection methods that could be used to efficiently scan genomes to detect novel ncRNAs. However, unlike coding genes, ncRNAs often lack distinctive sequence features that could be used for recognizing them. Although many ncRNAs are known to have a well conserved secondary structure, which provides useful cues for computational prediction, it has been also shown that a structure-based approach alone may not be sufficient for detecting ncRNAs in a single sequence. Currently, the most effective ncRNA detection methods combine structure-based techniques with a comparative genome analysis approach to improve the prediction performance. In this paper, we propose RNAdetect, a computational method incorporating novel features for accurate detection of ncRNAs in combination with comparative genome analysis. Given a sequence alignment, RNAdetect can accurately detect the presence of functional ncRNAs by incorporating novel predictive features based on the concept of generalized ensemble defect (GED), which assesses the degree of structure conservation across multiple related sequences and the conformation of the individual folding structures to a common consensus structure. Furthermore, n-gram models (NGMs) are used to extract features that can effectively capture sequence homology to known ncRNA families. Utilization of NGMs can enhance the detection of ncRNAs that have sparse folding structures with many unpaired bases. Extensive performance evaluation based on the Rfam database and bacterial genomes demonstrate that RNAdetect can accurately and reliably detect novel ncRNAs, outperforming the current state-of-the-art methods. The source code for RNAdetect and the benchmark data used in this paper can be downloaded at https://github.com/bjyoontamu/RNAdetect.

Algorithms for matching partially labelled sequence graphs

BackgroundIn order to find correlated pairs of positions between proteins, which are useful in predicting interactions, it is necessary to concatenate two large multiple sequence alignments such that the sequences that are joined together belong to those that interact in their species of origin. When each protein is unique then the species name is sufficient to guide this match, however, when there are multiple related sequences (paralogs) in each species then the pairing is more difficult. In bacteria a good guide can be gained from genome co-location as interacting proteins tend to be in a common operon but in eukaryotes this simple principle is not sufficient.ResultsThe methods developed in this paper take sets of paralogs for different proteins found in the same species and make a pairing based on their evolutionary distance relative to a set of other proteins that are unique and so have a known relationship (singletons). The former constitute a set of unlabelled nodes in a graph while the latter are labelled. Two variants were tested, one based on a phylogenetic tree of the sequences (the topology-based method) and a simpler, faster variant based only on the inter-sequence distances (the distance-based method). Over a set of test proteins, both gave good results, with the topology method performing slightly better.ConclusionsThe methods develop here still need refinement and augmentation from constraints other than the sequence data alone, such as known interactions from annotation and databases, or non-trivial relationships in genome location. With the ever growing numbers of eukaryotic genomes, it is hoped that the methods described here will open a route to the use of these data equal to the current success attained with bacterial sequences.

RNA motif discovery: a computational overview.

Genomic studies have greatly expanded our knowledge of structural non-coding RNAs (ncRNAs). These RNAs fold into characteristic secondary structures and perform specific-structure dependent biological functions. Hence RNA secondary structure prediction is one of the most well studied problems in computational RNA biology. Comparative sequence analysis is one of the more reliable RNA structure prediction approaches as it exploits information of multiple related sequences to infer the consensus secondary structure. This class of methods essentially learns a global secondary structure from the input sequences. In this paper, we consider the more general problem of unearthing common local secondary structure based patterns from a set of related sequences. The input sequences for example could correspond to 3′ or 5′ untranslated regions of a set of orthologous genes and the unearthed local patterns could correspond to regulatory motifs found in these regions. These sequences could also correspond to in vitro selected RNA, genomic segments housing ncRNA genes from the same family and so on. Here, we give a detailed review of the various computational techniques proposed in literature attempting to solve this general motif discovery problem. We also give empirical comparisons of some of the current state of the art methods and point out future directions of research.ReviewersThis article was reviewed by Dr. Erez Levanon, Dr. Sebastian Maurer-Stroh and Dr. Weixiong Zhang.

AntiSMASH 2.0—a versatile platform for genome mining of secondary metabolite producers

Microbial secondary metabolites are a potent source of antibiotics and other pharmaceuticals. Genome mining of their biosynthetic gene clusters has become a key method to accelerate their identification and characterization. In 2011, we developed antiSMASH, a web-based analysis platform that automates this process. Here, we present the highly improved antiSMASH 2.0 release, available at http://antismash.secondarymetabolites.org/. For the new version, antiSMASH was entirely re-designed using a plug-and-play concept that allows easy integration of novel predictor or output modules. antiSMASH 2.0 now supports input of multiple related sequences simultaneously (multi-FASTA/GenBank/EMBL), which allows the analysis of draft genomes comprising multiple contigs. Moreover, direct analysis of protein sequences is now possible. antiSMASH 2.0 has also been equipped with the capacity to detect additional classes of secondary metabolites, including oligosaccharide antibiotics, phenazines, thiopeptides, homo-serine lactones, phosphonates and furans. The algorithm for predicting the core structure of the cluster end product is now also covering lantipeptides, in addition to polyketides and non-ribosomal peptides. The antiSMASH ClusterBlast functionality has been extended to identify sub-clusters involved in the biosynthesis of specific chemical building blocks. The new features currently make antiSMASH 2.0 the most comprehensive resource for identifying and analyzing novel secondary metabolite biosynthetic pathways in microorganisms.

Quantitative measurement of pathogen specific human memory T cell repertoire diversity using a CDR3β-specific microarray

BackgroundProviding quantitative microarray data that is sensitive to very small differences in target sequence would be a useful tool in any number of venues where a sample can consist of a multiple related sequences present in various abundances. Examples of such applications would include measurement of pseudo species in viral infections and the measurement of species of antibodies or T cell receptors that constitute immune repertoires. Difficulties that must be overcome in such a method would be to account for cross-hybridization and for differences in hybridization efficiencies between the arrayed probes and their corresponding targets. We have used the memory T cell repertoire to an influenza-derived peptide as a test case for developing such a method.ResultsThe arrayed probes were corresponded to a 17 nucleotide TCR-specific region that distinguished sequences differing by as little as a single nucleotide. Hybridization efficiency between highly related Cy5-labeled subject sequences was normalized by including an equimolar mixture of Cy3-labeled synthetic targets representing all 108 arrayed probes. The same synthetic targets were used to measure the degree of cross hybridization between probes. Reconstitution studies found the system sensitive to input ratios as low as 0.5% and accurate in measuring known input percentages (R2 = 0.81, R = 0.90, p < 0.0001). A data handling protocol was developed to incorporate the differences in hybridization efficiency. To validate the array in T cell repertoire analysis, it was used to analyze human recall responses to influenza in three human subjects and compared to traditional cloning and sequencing. When evaluating the rank order of clonotype abundance determined by each method, the approaches were not found significantly different (Wilcoxon rank-sum test, p > 0.05).ConclusionThis novel strategy appears to be robust and can be adapted to any situation where complex mixtures of highly similar sequences need to be quantitatively resolved.

The genomic context of natural killer receptor extended gene families.

The two sets of inhibitory and activating natural killer (NK) receptor genes belong either to the Ig or to the C-type lectin superfamilies. Both are extensive and diverse, comprising genes of varying degrees of relatedness, indicative of a process of iterative duplication. We have constructed gene maps to help understand how and when NK receptor genes developed and the nature of their polymorphism. A cluster of over 15 C-type lectin genes, the natural killer complex is located on human chromosome 12p13.1, syntenic with a region in mouse that borders multiple Ly49 loci. The equivalent locus in man is occupied by a single pseudogene, LY49L. The immunoglobulin superfamily of loci, the leukocyte receptor complex (LRC), on chromosome 19q13.4, contains many polymorphic killer cell immunoglobulin-like receptor (KIR) genes as well as multiple related sequences. These include immunoglobulin-like transcript (ILT) (or leukocyte immunoglobulin-like receptor genes), leukocyte-associated inhibitory receptor genes (LAIR), NKp46, Fc alphaR and the platelet glycoprotein receptor VI locus, which encodes a collagen-binding molecule. KIRs are expressed mostly on NK cells and some T cells. The other LRC loci are more widely expressed. Further centromeric of the LRC are sets of additional loci with weak sequence similarity to the KIRs, including the extensive CD66(CEA) and Siglec families. The LRC-syntenic region in mice contains no orthologues of KIRs. Some of the KIR genes are highly polymorphic in terms of sequence as well as for presence/absence of genes on different haplotypes. Some anchor loci, such as KIR2DL4, are present on most haplotypes. A few ILT loci, such as ILT5 and ILT8, are polymorphic, but only ILT6 exhibits presence/absence variation. This knowledge of the genomic organisation of the extensive NK superfamilies underpins efforts to understand the functions of the encoded NK receptor molecules. It leads to the conclusion that the functional homology of human KIR and mouse Ly49 genes arose by convergent evolution. NK receptor immunogenetics has interesting parallels with the major histocompatibility complex (MHC) in which some of the polymorphic genes are ligands for NK molecules. There are hints of an ancient genetic relationship between NK receptor genes and MHC-paralogous regions on chromosomes 1, 9 and 19. The picture that emerges from both complexes is of eternal evolutionary restlessness, presumably in response to resistance to disease.

Protein evolution by molecular breeding

Natural evolution has guided the development of 'molecular breeding' processes used in the laboratory for the rapid modification of subgenomic sequences including single genes. The most significant recent development has been the in vitro permutation of natural diversity. Homologous recombination of multiple related sequences produced high-quality libraries of chimeric sequences encoding proteins with functions that differ dramatically from any of the parents. Increasingly powerful screening methods are also being developed, allowing these libraries to be screened for novel biocatalysts.

Identification and characterization of a new major histocompatibility complex class I gene in carp (Cyprinus carpio L.)

In this study we report the finding of three representatives of a new group of major histocompatibility complex class I sequences in carp: Cyca-12 (Cyca-UA1*01), a full-length cDNA; Cyca-SP1 (Cyca-UAW1), a polymerase chain reaction (PCR) fragment from cDNA; and Cyca-G11 (Cyca-UA1*02), a partial genomic clone. Comparison of the amino acid sequences of Cyca-12, Cyca-SP1, and Cyca-G11 with classical and non-classical class I sequences from other species shows considerable conservation in regions that have been shown to be involved in maintaining the structure and function of class I molecules. The genomic organization of Cyca-12 has been elucidated by analysis of a partial genomic clone (Cyca-G11, in combination with PCR amplifications on genomic DNA of a homozygous individual. Although the genomic organization is similar to that found in class I genes from other species, the 3' untranslated region contains an intron which is unprecedented in class I genes, and intron 2 is exceptionally large (+/-14 kilobases). Southern blot analysis indicates the presence of multiple related sequences. In phylogenetic analyses, the Cyca-UA sequences cluster with class I genes from zebrafish and Atlantic salmon, indicating that the ancestral gene arose before the salmonid/cyprinid split, approximately 120-150 million years ago. The previously reported class I Cyca-Z genes from carp and Caau-Z genes from goldfish cluster as a completely separate lineage. A polyclonal antiserum (anti-Cyca12) was raised against a recombinant fusion protein containing most of the extracellular domains of Cyca-12. The antibodies showed substantial reactivity to the recombinant protein and an Mr 45000 protein in membrane lysates of spleen and muscle, as well as to determinants present on leucocytes in fluorescence-activated cell sorter analyses. Erythrocytes and thrombocytes were found to be negative.

Isolation and Mapping of Human T-Cell Protein Tyrosine Phosphatase Sequences: Localization of Genes and Pseudogenes Discriminated Using Fluorescence Hybridization with Genomic versus cDNA Probes

This work reports the isolation, partial characterization, and chromosomal mapping of several human T-cell protein tyrosine phosphatase (PTPase) sequences and provides a direct comparison of the specificity of cDNA versus genomic probes in discriminating the location of genes versus pseudogenes by fluorescence in situ hybridization. In initial attempts to map the T-cell (TC) PTP gene using a 2-kb cDNA, several labeled sites were noted, raising the possibility of multiple related sequences within the genome. To address this, four genomic clones were obtained with homology to the TC PTP cDNA and characterized for their primary structure and their position within the human genome. Based on the presence or absence of an open reading frame and the intron/exon structure, two of these clones were found to be overlapping sequences encoding the true TC PTP gene and two were highly related but distinct processed pseudogenes. The TC PTP gene (gene symbol PTPN2) encoded by clones L17-2 and L5-1 localized to chromosome 18p11.2-p11.3, whereas pseudogenes encoded by clone L17-1, entitled TCPS1 (gene symbol PTPN2P1), and clone L18, entitled TCPS13 (gene symbol PTPN2P2), mapped to chromosomes 1q22-q24 and 13q12-q13, respectively. A direct comparison of the specificity of genomic and cDNA probes demonstrated that under identical conditions the genomic probes (containing both exon and intron sequences) readily identified a single specific site of hybridization, whereas the cDNA identified sites of both the gene and its pseudogenes. While providing mapping and sequencing information on the TC PTPase sequences, this work illustrates a strategy for addressing a recurrent problem in gene mapping studies where highly related sequences exist within the genome.