Identification Of Gene Structures Research Articles

Genome-wide identification, characterization and expression analysis of non-RD receptor like kinase gene family under Colletotrichum truncatum stress conditions in hot pepper.

Receptor like kinases (RLKs) are preserved upstream signaling molecules which regulate several biological processes from plant development to various stress adaptation programs. Non arginine aspartate (non-RD) a prominent class of RLKs plays a significant role in disease resistance and apoptosis in plants. In present investigation, a comprehensive in silico analysis for non-RD Kinase gene family as well as identification of gene structures, sequence similarity, chromosomal localization, gene duplication analysis, promoter analysis, transcript expression profiles and phylogenic studies were done. In this study, twenty-six genes were observed on nine out of twelve chromosomes. All these genes were clustered into five subfamilies under large monophyletic group termed as Interleukin-1 Receptor-Associated Kinase (IRAK) family. Some of the important physiochemical properties of twenty-six proteins are determined and ranged in the following order: (a) Amino acids size ranged from (620 to 1781) (b) Molecular weight ranged as of (70.11 to 197.11 KDa) and (c) Theoretical PI ranged from (5.69 to 8.63) respectively. Structural diversity in genomic structure among non-RD kinase gene family was identified and presence of pathogen induced cis regulatory elements including STRE, MYC, MYB, and W box were found. Expression profiles revealed the potential ability of three genes CaRLK1 from LRRXII and CaRLK15,16 from stress antifung subfamily were pointedly upregulated beyond the severe stress time period (9 DAI) in anthracnose resistant genotype PBC-80 in response to Colletotrichum truncatum infection. Subsequently, in silico studies from the available genome sequencing data helped us to identify candidate genes tangled in inducing disease resistance.

Alt-Splice Gene Predictor Using Multitrack-Clique Analysis: Verification of Statistical Support for Modelling in Genomes of Multicellular Eukaryotes

One of the main limitations of the typical hidden Markov model (HMM) implementation for gene structure identification is that a single structure is identified on a given sequence of genomic data—i.e., identification of overlapping structure is not directly possible, and certainly not possible within the confines of the optimal Viterbi path evaluation. This is a huge limitation given that we now know that significant portions of eukaryotic genomes, particularly mammalian genomes, are alternatively spliced, and, thus, have overlapping structure in the sense of the mRNA transcripts that result. Using the general meta-state HMM approach developed in prior work, however, more than one ‘track’ of annotation can be accommodated, thereby allowing a direct implementation of an alternative-splice gene-structure identifier. In this paper we examine the representation of alternative splicing annotation in the multi-track context, and show that the proliferation on states is manageable, and has sufficient statistical support on the genomes examined (human, mouse, worm, and fly) that a full alt-splice meta-state HMM gene finder can be implemented with sufficient statistical support. In the process of performing the alternative splicing analysis on alt-splice event counts we expected to see an increase in alternative splicing complexity as the organism becomes more complex, and this is seen with the percentage of genes with alt-splice variants increasing from worm to fly to the mammalian genomes (mouse and human). Of particular note is an increase in alternative splicing variants at the start and end of coding with the more complex organisms studied (mouse and human), indicating rapid new first and last exon recruitment that is possibly spliceosome mediated. This suggests that spliceosome-mediated refinements (acceleration) of gene structure variation and selection, with increasing levels of sophistication, has occurred in eukaryotes and in mammals especially.

RNA‐Seq Read Alignments with PALMapper

Next-generation sequencing technologies have revolutionized genome and transcriptome sequencing. RNA-Seq experiments are able to generate huge amounts of transcriptome sequence reads at a fraction of the cost of Sanger sequencing. Reads produced by these technologies are relatively short and error prone. To utilize such reads for transcriptome reconstruction and gene-structure identification, one needs to be able to accurately align the sequence reads over intron boundaries. In this unit, we describe PALMapper, a fast and easy-to-use tool that is designed to accurately compute both unspliced and spliced alignments for millions of RNA-Seq reads. It combines the efficient read mapper GenomeMapper with the spliced aligner QPALMA, which exploits read-quality information and predictions of splice sites to improve the alignment accuracy. The PALMapper package is available as a command-line tool running on Unix or Mac OS X systems or through a Web interface based on Galaxy tools.

A Metastate HMM with Application to Gene Structure Identification in Eukaryotes

We introduce a generalized-clique hidden Markov model (HMM) and apply it to gene finding in eukaryotes (C. elegans). We demonstrate a HMM structure identification platform that is novel and robustly-performing in a number of ways. The generalized clique HMM begins by enlarging the primitive hidden states associated with the individual base labels (as exon, intron, or junk) to substrings of primitive hidden states, or footprint states, having a minimal length greater than the footprint state length. The emissions are likewise expanded to higher order in the fundamental joint probability that is the basis of the generalized-clique, or "metastate", HMM. We then consider application to eukaryotic gene finding and show how such a metastate HMM improves the strength of coding/noncoding-transition contributions to gene-structure identification. We will describe situations where the coding/noncoding-transition modeling can effectively recapture the exon and intron heavy tail distribution modeling capability as well as manage the exon-start needle-in-the-haystack problem. In analysis of the C. elegans genome we show that the sensitivity and specificity (SN,SP) results for both the individual-state and full-exon predictions are greatly enhanced over the standard HMM when using the generalized-clique HMM.

Optimal spliced alignments of short sequence reads

Motivation: Next generation sequencing technologies open exciting new possibilities for genome and transcriptome sequencing. While reads produced by these technologies are relatively short and error prone compared to the Sanger method their throughput is several magnitudes higher. To utilize such reads for transcriptome sequencing and gene structure identification, one needs to be able to accurately align the sequence reads over intron boundaries. This represents a significant challenge given their short length and inherent high error rate. Results: We present a novel approach, called QPALMA, for computing accurate spliced alignments which takes advantage of the read's quality information as well as computational splice site predictions. Our method uses a training set of spliced reads with quality information and known alignments. It uses a large margin approach similar to support vector machines to estimate its parameters to maximize alignment accuracy. In computational experiments, we illustrate that the quality information as well as the splice site predictions help to improve the alignment quality. Finally, to facilitate mapping of massive amounts of sequencing data typically generated by the new technologies, we have combined our method with a fast mapping pipeline based on enhanced suffix arrays. Our algorithms were optimized and tested using reads produced with the Illumina Genome Analyzer for the model plant Arabidopsis thaliana. Availability: Datasets for training and evaluation, additional results and a stand-alone alignment tool implemented in C++ and python are available at http://www.fml.mpg.de/raetsch/projects/qpalma. Contact: Gunnar.Raetsch@tuebingen.mpg.de

Optimal spliced alignments of short sequence reads

Next generation sequencing technologies open exciting new possibilities for genome and transcriptome sequencing. While reads produced by these technologies are relatively short and error prone compared to the Sanger method their throughput is several magnitudes higher. To utilize such reads for transcriptome sequencing and gene structure identification, one needs to be able to accurately align the sequence reads over intron boundaries. This represents a significant challenge given their short length and inherent high error rate. We present a novel approach, called QPALMA, for computing accurate spliced alignments which takes advantage of the read's quality information as well as computational splice site predictions. Our method uses a training set of spliced reads with quality information and known alignments. It uses a large margin approach similar to support vector machines to estimate its parameters to maximize alignment accuracy. In computational experiments, we illustrate that the quality information as well as the splice site predictions help to improve the alignment quality. Finally, to facilitate mapping of massive amounts of sequencing data typically generated by the new technologies, we have combined our method with a fast mapping pipeline based on enhanced suffix arrays. Our algorithms were optimized and tested using reads produced with the Illumina Genome Analyzer for the model plant Arabidopsis thaliana. Datasets for training and evaluation, additional results and a stand-alone alignment tool implemented in C++ and python are available at http://www.fml.mpg.de/raetsch/projects/qpalma.

A novel, fast, HMM-with-Duration implementation – for application with a new, pattern recognition informed, nanopore detector

BackgroundHidden Markov Models (HMMs) provide an excellent means for structure identification and feature extraction on stochastic sequential data. An HMM-with-Duration (HMMwD) is an HMM that can also exactly model the hidden-label length (recurrence) distributions – while the regular HMM will impose a best-fit geometric distribution in its modeling/representation.ResultsA Novel, Fast, HMM-with-Duration (HMMwD) Implementation is presented, and experimental results are shown that demonstrate its performance on two-state synthetic data designed to model Nanopore Detector Data. The HMMwD experimental results are compared to (i) the ideal model and to (ii) the conventional HMM. Its accuracy is clearly an improvement over the standard HMM, and matches that of the ideal solution in many cases where the standard HMM does not. Computationally, the new HMMwD has all the speed advantages of the conventional (simpler) HMM implementation. In preliminary work shown here, HMM feature extraction is then used to establish the first pattern recognition-informed (PRI) sampling control of a Nanopore Detector Device (on a "live" data-stream).ConclusionThe improved accuracy of the new HMMwD implementation, at the same order of computational cost as the standard HMM, is an important augmentation for applications in gene structure identification and channel current analysis, especially PRI sampling control, for example, where speed is essential. The PRI experiment was designed to inherit the high accuracy of the well characterized and distinctive blockades of the DNA hairpin molecules used as controls (or blockade "test-probes"). For this test set, the accuracy inherited is 99.9%.

Hidden Markov Model Variants and their Application

Markov statistical methods may make it possible to develop an unsupervised learning process that can automatically identify genomic structure in prokaryotes in a comprehensive way. This approach is based on mutual information, probabilistic measures, hidden Markov models, and other purely statistical inputs. This approach also provides a uniquely common ground for comparative prokaryotic genomics. The approach is an on-going effort by its nature, as a multi-pass learning process, where each round is more informed than the last, and thereby allows a shift to the more powerful methods available for supervised learning at each iteration. It is envisaged that this "bootstrap" learning process will also be useful as a knowledge discovery tool. For such an ab initio prokaryotic gene-finder to work, however, it needs a mechanism to identify critical motif structure, such as those around the start of coding or start of transcription (and then, hopefully more).For eukaryotes, even with better start-of-coding identification, parsing of eukaryotic coding regions by the HMM is still limited by the HMM's single gene assumption, as evidenced by the poor performance in alternatively spliced regions. To address these complications an approach is described to expand the states in a eukaryotic gene-predictor HMM, to operate with two layers of DNA parsing. This extension from the single layer gene prediction parse is indicated after preliminary analysis of the C. elegans alt-splice statistics. State profiles have made use of a novel hash-interpolating MM (hIMM) method. A new implementation for an HMM-with-Duration is also described, with far-reaching application to gene-structure identification and analysis of channel current blockade data.

Gene structure identification with MyGV using cDNA evidence and protein homologs to improve ab initio predictions.

MyGV is an application to visualize (potentially genome-scale) gene structure annotation and prediction. The output of any external gene prediction program can be easily converted to a generalized format for input into MyGV. The application displays all input simultaneously in graphical representation, with a toggle option for a text-based view. Zooming capabilities allow detailed comparisons for specific genome locations. The tool is particularly helpful for refinement of ab initio predicted gene structures by spliced alignment with cDNA or protein homologs. The program was written in Java and is freely available to non-commercial users by electronic download from http://bioinformatics.iastate.edu/bioinformatics2go/MyGV.

Improved splice site detection in Genie.

We present an improved splice site predictor for the genefinding program Genie. Genie is based on a generalized Hidden Markov Model (GHMM) that describes the grammar of a legal parse of a multi-exon gene in a DNA sequence. In Genie, probabilities are estimated for gene features by using dynamic programming to combine information from multiple content and signal sensors, including sensors that integrate matches to homologous sequences from a database. One of the hardest problems in genefinding is to determine the complete gene structure correctly. The splice site sensors are the key signal sensors that address this problem. We replaced the existing splice site sensors in Genie with two novel neural networks based on dinucleotide frequencies. Using these novel sensors, Genie shows significant improvements in the sensitivity and specificity of gene structure identification. Experimental results in tests using a standard set of annotated genes showed that Genie identified 86% of coding nucleotides correctly with a specificity of 85%, versus 80% and 84% in the older system. In further splice site experiments, we also looked at correlations between splice site scores and intron and exon lengths, as well as at the effect of distance to the nearest splice site on false positive rates.