Suresh B. Mudunuri

IMEx: Imperfect Microsatellite Extractor

MOTIVATION Microsatellites, also known as simple sequence repeats, are the tandem repeats of nucleotide motifs of the size 1-6 bp found in every genome known so far. Their importance in genomes is well known. Microsatellites are associated with various disease genes, have been used as molecular markers in linkage analysis and DNA fingerprinting studies, and also seem to play an important role in the genome evolution. Therefore, it is of importance to study distribution, enrichment and polymorphism of microsatellites in the genomes of interest. For this, the prerequisite is the availability of a computational tool for extraction of microsatellites (perfect as well as imperfect) and their related information from whole genome sequences. Examination of available tools revealed certain lacunae in them and prompted us to develop a new tool. RESULTS In order to efficiently screen genome sequences for microsatellites (perfect as well as imperfect), we developed a new tool called IMEx (Imperfect Microsatellite Extractor). IMEx uses simple string-matching algorithm with sliding window approach to screen DNA sequences for microsatellites and reports the motif, copy number, genomic location, nearby genes, mutational events and many other features useful for in-depth studies. IMEx is more sensitive, efficient and useful than the available widely used tools. IMEx is available in the form of a stand-alone program as well as in the form of a web-server. AVAILABILITY A World Wide Web server and the stand-alone program are available for free access at http://203.197.254.154/IMEX/ or http://www.cdfd.org.in/imex.

Meta-analysis of tRNA derived RNA fragments reveals that they are evolutionarily conserved and associate with AGO proteins to recognize specific RNA targets

BackgroundtRFs, 14 to 32 nt long single-stranded RNA derived from mature or precursor tRNAs, are a recently discovered class of small RNA that have been found to be present in diverse organisms at read counts comparable to miRNAs. Currently, there is a debate about their biogenesis and function.ResultsThis is the first meta-analysis of tRFs. Analysis of more than 50 short RNA libraries has revealed that tRFs are precisely generated fragments present in all domains of life (bacteria to humans), and are not produced by the miRNA biogenesis pathway. Human PAR-CLIP data shows a striking preference for tRF-5s and tRF-3s to associate with AGO1, 3 and 4 rather than AGO2, and analysis of positional T to C mutational frequency indicates these tRFs associate with Argonautes in a manner similar to miRNAs. The reverse complements of canonical seed positions in these sequences match cross-link centered regions, suggesting these tRF-5s and tRF-3s interact with RNAs in the cell. Consistent with these results, human AGO1 CLASH data contains thousands of tRF-5 and tRF-3 reads chimeric with mRNAs.ConclusionstRFs are an abundant class of small RNA present in all domains of life whose biogenesis is distinct from miRNAs. In human HEK293 cells tRFs associate with Argonautes 1, 3 and 4 and not Argonaute 2 which is the main effector protein of miRNA function, but otherwise have very similar properties to miRNAs, indicating tRFs may play a major role in RNA silencing.

tRFdb: a database for transfer RNA fragments

We have created tRFdb, the first database of transfer RNA fragments (tRFs), available at http://genome.bioch.virginia.edu/trfdb/. With over 100 small RNA libraries analyzed, the database currently contains the sequences and read counts of the three classes of tRFs for eight species: R. sphaeroides, S. pombe, D. melanogaster, C. elegans, Xenopus, zebra fish, mouse and human, for a total of 12 877 tRFs. The database can be searched by tRF ID or tRF sequence, and the results can be limited by organism. The search results show the genome coordinates and names of the tRNAs the sequence may derive from, and there are links for the sequence of the tRF and parental tRNA, and links for the read counts in all the corresponding small RNA libraries. As a case study for how this database may be used, we have shown that a certain class of tRFs, tRF-1s, is highly upregulated in B-cell malignancies.

ChloroMitoSSRDB: Open Source Repository of Perfect and Imperfect Repeats in Organelle Genomes for Evolutionary Genomics

Microsatellites or simple sequence repeats (SSRs) are repetitive stretches of nucleotides (A, T, G, C) that are distributed either as single base pair stretches or as a combination of two- to six-nucleotides units that are non-randomly distributed within coding and in non-coding regions of the genome. ChloroMitoSSRDB is a complete curated web-oriented relational database of perfect and imperfect repeats in organelle genomes. The present version of the database contains perfect and imperfect SSRs of 2161 organelle genomes (1982 mitochondrial and 179 chloroplast genomes). We detected a total of 5838 chloroplast perfect SSRs, 37 297 chloroplast imperfect SSRs, 5898 mitochondrial perfect SSRs and 50 355 mitochondrial imperfect SSRs across these genomes. The repeats have been further hyperlinked to the annotated gene regions (coding or non-coding) and a link to the corresponding gene record in National Center for Biotechnology Information(www.ncbi.nlm.nih.gov/) to identify and understand the positional relationship of the repetitive tracts. ChloroMitoSSRDB is connected to a user-friendly web interface that provides useful information associated with the location of the repeats (coding and non-coding), size of repeat, motif and length polymorphism, etc. ChloroMitoSSRDB will serve as a repository for developing functional markers for molecular phylogenetics, estimating molecular variation across species. Database URL: ChloroMitoSSRDB can be accessed as an open source repository at www.mcr.org.in/chloromitossrdb.

Performance Analysis and Evaluation of Different Data Mining Algorithms used for Cancer Classification

Classification algorithms of data mining have been successfully applied in the recent years to predict cancer based on the gene expression data. Micro-array is a powerful diagnostic tool that can generate handful information of gene expression of all the human genes in a cell at once. Various classification algorithms can be applied on such micro-array data to devise methods that can predict the occurrence of tumor. However, the accuracy of such methods differ according to the classification algorithm used. Identifying the best classification algorithm among all available is a challenging task. In this study, we have made a comprehensive comparative analysis of 14 different classification algorithms and their performance has been evaluated by using 3 different cancer data sets. The results indicate that none of the classifiers outperformed all others in terms of the accuracy when applied on all the 3 data sets. Most of the algorithms performed better as the size of the data set is increased. We recommend the users not to stick to a particular classification method and should evaluate different classification algorithms and select the better algorithm.

ChloroMitoSSRDB 2.00: more genomes, more repeats, unifying SSRs search patterns and on-the-fly repeat detection

Organelle genomes evolve rapidly as compared with nuclear genomes and have been widely used for developing microsatellites or simple sequence repeats (SSRs) markers for delineating phylogenomics. In our previous reports, we have established the largest repository of organelle SSRs, ChloroMitoSSRDB, which provides access to 2161 organelle genomes (1982 mitochondrial and 179 chloroplast genomes) with a total of 5838 perfect chloroplast SSRs, 37 297 imperfect chloroplast SSRs, 5898 perfect mitochondrial SSRs and 50 355 imperfect mitochondrial SSRs across organelle genomes. In the present research, we have updated ChloroMitoSSRDB by systematically analyzing and adding additional 191 chloroplast and 2102 mitochondrial genomes. With the recent update, ChloroMitoSSRDB 2.00 provides access to a total of 4454 organelle genomes displaying a total of 40 653 IMEx Perfect SSRs (11 802 Chloroplast Perfect SSRs and 28 851 Mitochondria Perfect SSRs), 275 981 IMEx Imperfect SSRs (78 972 Chloroplast Imperfect SSRs and 197 009 Mitochondria Imperfect SSRs), 35 250 MISA (MIcroSAtellite identification tool) Perfect SSRs and 3211 MISA Compound SSRs and associated information such as location of the repeats (coding and non-coding), size of repeat, motif and length polymorphism, and primer pairs. Additionally, we have integrated and made available several in silico SSRs mining tools through a unified web-portal for in silico repeat mining for assembled organelle genomes and from next generation sequencing reads. ChloroMitoSSRDB 2.00 allows the end user to perform multiple SSRs searches and easy browsing through the SSRs using two repeat algorithms and provide primer pair information for identified SSRs for evolutionary genomics. Database URL: http://www.mcr.org.in/chloromitossrdb

G-IMEx: A comprehensive software tool for detection of microsatellites from genome sequences.

Microsatellites are ubiquitous short tandem repeats found in all known genomes and are known to play a very important role in various studies and fields including DNA fingerprinting, paternity studies, evolutionary studies, virulence and adaptation of certain bacteria and viruses etc. Due to the sequencing of several genomes and the availability of enormous amounts of sequence data during the past few years, computational studies of microsatellites are of interest for many researchers. In this context, we developed a software tool called Imperfect Microsatellite Extractor (IMEx), to extract perfect, imperfect and compound microsatellites from genome sequences along with their complete statistics. Recently we developed a user-friendly graphical-interface using JAVA for IMEx to be used as a stand-alone software named G-IMEx. G-IMEx takes a nucleotide sequence as an input and the results are produced in both html and text formats. The Linux version of G-IMEx can be downloaded for free from http://www.cdfd.org.in/imex

Comparative analysis of microsatellite detecting software: a significant variation in results and influence of parameters

Microsatellites are a unique type of repeat patterns found in genome sequences of all known organisms including bacteria and viruses. These repeats play an important role in genome evolution, are associated with various diseases, have been used as molecular markers in DNA Fingerprinting, Population Genetics etc. Various bioinformatics tools have been developed for extraction of microsatellites from DNA sequences. However, not all tools can identify microsatellites with similar sensitivities and hence studies on microsatellites can suffer from significant biases in results and interpretations depending on the type of tool used. In order to get a clear idea on inherent limitations and biases with regard to extraction of microsatellites especially under the influence of varying threshold values of program parameters we carried out a comparative analysis of performance of some of the widely used tools using some test DNA sequences. We extracted imperfect microsatellites from three different sequences (E. coli bacterial genome, C. elegans Chromosome I and Drosophila Chromosome X) using the commonly used microsatellite extraction tools TRF, Sputnik, SciRoKoCo and IMEx with varying parameters and analyzed the results. We observed a significant variation in the number of microsatellites extracted by these tools even when used with default / suggested parameters. Relaxation of parameter values lead to an increase in the number of repeats detected but still the differences among the results persist. In TRF, Sputnik and SciRoKoCo it was observed that the number of mismatches increases with the increase in the tract length of the repeat indicating the level of imperfection is not uniform throughout the repeats. The four tools investigated in this study differ in their algorithms, in the parameters they use and hence in the number of microsatellites detected. The score based programs identify more number of divergent penta and hexa nucleotide repeats than IMEx. We therefore suggest that it is prudent to alter parameters appropriately to detect as many microsatellites as possible as a means not to miss any genuine repeat tracts or to use more than one tool as a means to get a good consensus. We also made a detailed survey of the available features of all microsatellite extraction tools. Apart from differences in their algorithm, efficiency and parameters, the tools also differ largely in terms of the features and flexibility.

MICdb3.0: a comprehensive resource of microsatellite repeats from prokaryotic genomes

The MICdb is a comprehensive relational database of perfect microsatellites extracted from completely sequenced and annotated genomes of bacteria and archaea. The current version MICdb3.0 is an updated and revised version of MICdb2.0. As compared with the previous version MICdb2.0, the current release is significantly improved in terms of much larger coverage of genomes, improved presentation of queried results, user-friendly administration module to manage Simple Sequence Repeat (SSR) data such as addition of new genomes, deletion of obsolete data, etc., and also removal of certain features deemed to be redundant. The new web-interface to the database called Microsatellite Analysis Server (MICAS) version 3.0 has been improved by the addition of powerful high-quality visualization tools to view the query results in the form of pie charts and bar graphs. All the query results and graphs can be exported in different formats so that the users can use them for further analysis. MICAS3.0 is also equipped with a unique genome comparison module using which users can do pair-wise comparison of genomes with regard to their microsatellite distribution. The advanced search module can be used to filter the repeats based on certain criteria such as filtering repeats of a particular motif/repeat size, extracting repeats of coding/non-coding regions, sort repeats, etc. The MICdb database has, therefore, been made portable to be administered by a person with the necessary administrative privileges. The MICdb3.0 database and analysis server can be accessed for free from www.cdfd.org.in/micas. Database URL: http://www.cdfd.org.in/micas

Microsatellite Repeats in Mitochondrial Genomes: A Bioinformatic Analysis

Mitochondria also known as the powerhouses of the cells have their own DNA sequence and exhibit sequence similarity with bacterial genomes. Microsatellites are a special class of DNA repeats that are found to be helpful to understand evolution, diseases, and phylogeny are widely used in various applications including DNA Fingerprinting, Forensics, Paternity Studies and Linkage Analysis etc. These repeats are ubiquitously present in all genomes including mitochondrial genomes and very less is known about their distribution in organelle genomes. In this study, we have analyzed more than 4000 mitochondrial genomes and a detailed report on the distribution, frequency and variation of microsatellites in mitochondrial genomes has been presented.