Biomedical subjects

Lakshmi K Matukumalli

Publications and source records attributed to Lakshmi K Matukumalli.

6 recordsLinked to original sources

PubMed2006

Discovery and profiling of bovine microRNAs from immune-related and embryonic tissues.

MicroRNAs are small approximately 22 nucleotide-long noncoding RNAs capable of controlling gene expression by inhibiting translation. Alignment of human microRNA stem-loop sequences (mir) against a recent draft sequence assembly of the bovine genome resulted in identification of 334 predicted bovine mir. We sequenced five tissue-specific cDNA libraries derived from the small RNA fractions of bovine embryo, thymus, small intestine, and lymph node to validate these predictions and identify new mir. This strategy combined with comparative sequence analysis identified 129 sequences that corresponded to mature microRNAs (miR). A total of 107 sequences aligned to known human mir, and 100 of these matched expressed miR. The other seven sequences represented novel miR expressed from the complementary strand of previously characterized human mir. The 22 sequences without matches displayed characteristic mir secondary structures when folded in silico, and 10 of these retained sequence conservation with other vertebrate species. Expression analysis based on sequence identity counts revealed that some miR were preferentially expressed in certain tissues, while bta-miR-26a and bta-miR-103 were prevalent in all tissues examined. These results support the premise that species differences in regulation of gene expression by miR occur primarily at the level of expression and processing.

Animals↗

PubMed2006

SNP-PHAGE--High throughput SNP discovery pipeline.

BACKGROUND: Single nucleotide polymorphisms (SNPs) as defined here are single base sequence changes or short insertion/deletions between or within individuals of a given species. As a result of their abundance and the availability of high throughput analysis technologies SNP markers have begun to replace other traditional markers such as restriction fragment length polymorphisms (RFLPs), amplified fragment length polymorphisms (AFLPs) and simple sequence repeats (SSRs or microsatellite) markers for fine mapping and association studies in several species. For SNP discovery from chromatogram data, several bioinformatics programs have to be combined to generate an analysis pipeline. Results have to be stored in a relational database to facilitate interrogation through queries or to generate data for further analyses such as determination of linkage disequilibrium and identification of common haplotypes. Although these tasks are routinely performed by several groups, an integrated open source SNP discovery pipeline that can be easily adapted by new groups interested in SNP marker development is currently unavailable. RESULTS: We developed SNP-PHAGE (SNP discovery Pipeline with additional features for identification of common haplotypes within a sequence tagged site (Haplotype Analysis) and GenBank (-dbSNP) submissions. This tool was applied for analyzing sequence traces from diverse soybean genotypes to discover over 10,000 SNPs. This package was developed on UNIX/Linux platform, written in Perl and uses a MySQL database. Scripts to generate a user-friendly web interface are also provided with common queries for preliminary data analysis. A machine learning tool developed by this group for increasing the efficiency of SNP discovery is integrated as a part of this package as an optional feature. The SNP-PHAGE package is being made available open source at http://bfgl.anri.barc.usda.gov/ML/snp-phage/. CONCLUSION: SNP-PHAGE provides a bioinformatics solution for high throughput SNP discovery, identification of common haplotypes within an amplicon, and GenBank (dbSNP) submissions. SNP selection and visualization are aided through a user-friendly web interface. This tool is useful for analyzing sequence tagged sites (STSs) of genomic sequences, and this software can serve as a starting point for groups interested in developing SNP markers.

Base Sequence↗

PubMed2006

Genomic divergences among cattle, dog and human estimated from large-scale alignments of genomic sequences.

BACKGROUND: Approximately 11 Mb of finished high quality genomic sequences were sampled from cattle, dog and human to estimate genomic divergences and their regional variation among these lineages. RESULTS: Optimal three-way multi-species global sequence alignments for 84 cattle clones or loci (each >50 kb of genomic sequence) were constructed using the human and dog genome assemblies as references. Genomic divergences and substitution rates were examined for each clone and for various sequence classes under different functional constraints. Analysis of these alignments revealed that the overall genomic divergences are relatively constant (0.32-0.37 change/site) for pairwise comparisons among cattle, dog and human; however substitution rates vary across genomic regions and among different sequence classes. A neutral mutation rate (2.0-2.2 x 10(-9) change/site/year) was derived from ancestral repetitive sequences, whereas the substitution rate in coding sequences (1.1 x 10(-9) change/site/year) was approximately half of the overall rate (1.9-2.0 x 10(-9) change/site/year). Relative rate tests also indicated that cattle have a significantly faster rate of substitution as compared to dog and that this difference is about 6%. CONCLUSION: This analysis provides a large-scale and unbiased assessment of genomic divergences and regional variation of substitution rates among cattle, dog and human. It is expected that these data will serve as a baseline for future mammalian molecular evolution studies.

Animals↗

PubMed2006

Application of machine learning in SNP discovery.

BACKGROUND: Single nucleotide polymorphisms (SNP) constitute more than 90% of the genetic variation, and hence can account for most trait differences among individuals in a given species. Polymorphism detection software PolyBayes and PolyPhred give high false positive SNP predictions even with stringent parameter values. We developed a machine learning (ML) method to augment PolyBayes to improve its prediction accuracy. ML methods have also been successfully applied to other bioinformatics problems in predicting genes, promoters, transcription factor binding sites and protein structures. RESULTS: The ML program C4.5 was applied to a set of features in order to build a SNP classifier from training data based on human expert decisions (True/False). The training data were 27,275 candidate SNP generated by sequencing 1973 STS (sequence tag sites) (12 Mb) in both directions from 6 diverse homozygous soybean cultivars and PolyBayes analysis. Test data of 18,390 candidate SNP were generated similarly from 1359 additional STS (8 Mb). SNP from both sets were classified by experts. After training the ML classifier, it agreed with the experts on 97.3% of test data compared with 7.8% agreement between PolyBayes and experts. The PolyBayes positive predictive values (PPV) (i.e., fraction of candidate SNP being real) were 7.8% for all predictions and 16.7% for those with 100% posterior probability of being real. Using ML improved the PPV to 84.8%, a 5- to 10-fold increase. While both ML and PolyBayes produced a similar number of true positives, the ML program generated only 249 false positives as compared to 16,955 for PolyBayes. The complexity of the soybean genome may have contributed to high false SNP predictions by PolyBayes and hence results may differ for other genomes. CONCLUSION: A machine learning (ML) method was developed as a supplementary feature to the polymorphism detection software for improving prediction accuracies. The results from this study indicate that a trained ML classifier can significantly reduce human intervention and in this case achieved a 5-10 fold enhanced productivity. The optimized feature set and ML framework can also be applied to all polymorphism discovery software. ML support software is written in Perl and can be easily integrated into an existing SNP discovery pipeline.

Algorithms↗

PubMed2005

Expressed sequence tag analysis of Eimeria-stimulated intestinal intraepithelial lymphocytes in chickens.

Intraepithelial lymphocytes (IELs) play a critical role in protective immune response to intestinal pathogens such as Eimeria, the etiologic agent of avian coccidiosis. A list of genes expressed by intestinal IELs of Eimeria-infected chickens was compiled using the expressed sequence tag (EST) strategy. The 14,409 ESTs consisted of 1851 clusters and 7595 singletons, which revealed 9446 unique genes in the data set. Comparison of the sequence data with chicken DNA sequences in GenBank identified 125 novel clones. This EST library will provide a valuable resource for profiling global gene expression in normal and pathogen-infected chickens and identifying additional unique immune-related genes.

Animals↗

PubMed2004

EST-PAGE--managing and analyzing EST data.

UNLABELLED: EST-PAGE provides a bioinformatics solution for expressed sequence tags (EST) data entry, database management, GenBank submission, process control and data retrieval from a unified web interface that can be easily customized and adapted by groups working on diverse EST sequencing projects. AVAILABILITY: The system and source code are available upon request from the authors. SUPPLEMENTARY INFORMATION: http://EST-PAGE.binf.gmu.edu

Database Management Systems↗