Using Expressing Sequence Tags to Improve Gene Structure Annotation

Finding all gene structures is a crucial step in obtaining valuable information from genomic sequences. It is still a challenging problem, especially for vertebrate genomes, such as the human genome. Expressed Sequence Tags (ESTs) provide a tremendous resource for determining intron-exon structures. However, they are short and error prone, which prevents existing methods from exploiting EST information efficiently. This dissertation addresses three aspects of using ESTs for gene structure annotation. The first aspect is using ESTs to improve de novo gene prediction. Probability models are introduced for EST alignments to genomic sequence in exons, introns, interknit regions, splice sites and UTRs, representing the EST alignment patterns in these regions. New gene prediction systems were developed by combining the EST alignments with comparative genomics gene prediction systems, such as TWINSCAN and N-SCAN, so that they can predict gene structures more accurately where EST alignments exist without compromising their ability to predict gene structures where no EST exists. The accuracy of TWINSCAN_EST and NSCAN_EST is shown to be substantially better than any existing methods without using full-length cDNA or protein similarity information. The second aspect is using ESTs and de novo gene prediction to guide biology experiments, such as finding full ORF-containing-cDNA clones, which provide the most direct experimental evidence for gene structures. A probability model was introduced to guide experiments by summing over gene structure models consistent with EST alignments. The last aspect is a novel EST-to-genome alignment program called QPAIRAGON to improve the alignment accuracy by using EST sequencing quality values. Gene prediction accuracy can be improved by using this new EST-to-genome alignment program. It can also be used for many other bioinformatics applications, such as SNP finding and alternative splicing site prediction

Clinical manifestations in a Chinese girl with heterozygous de novo NAA10 variant c. 247C > T, p. (Arg83Cys): a case report

The NAA10 gene encodes the catalytic subunit of the N-terminal acetyltransferase protein complex A (NatA), which is supposed to acetylate approximately 40% of the human proteins. After the advent of next-generation sequencing, more variants in the NAA10 gene and Ogden syndrome (OMIM# 300855) have been reported. Individuals with NAA10-related syndrome have a wide spectrum of clinical manifestations and the genotype–phenotype correlation is still far from being confirmed. Here, we report a three years old Chinese girl carrying a heterozygous de novo NAA10 [NM_003491: c. 247C > T, p. (Arg83Cys)] variant (dbSNP# rs387906701) (ClinVar# 208664) (OMIM# 300013.0010). The proband not only has some mild and common clinical manifestations, including dysmorphic features, developmental delay, obstructive hypertrophic cardiomyopathy, and arrhythmia, but also shows some rare clinical features such as exophthalmos, blue sclera, cutaneous capillary malformations, and adenoid hypertrophy. Our attempt is to expand the clinical phenotype associated with NAA10-related syndrome and explore genotype–phenotype correlation with such syndrome

Pairagon+N-SCAN_EST: a model-based gene annotation pipeline

BACKGROUND: This paper describes Pairagon+N-SCAN_EST, a gene annotation pipeline that uses only native alignments. For each expressed sequence it chooses the best genomic alignment. Systems like ENSEMBL and ExoGean rely on trans alignments, in which expressed sequences are aligned to the genomic loci of putative homologs. Trans alignments contain a high proportion of mismatches, gaps, and/or apparently unspliceable introns, compared to alignments of cDNA sequences to their native loci. The Pairagon+N-SCAN_EST pipeline's first stage is Pairagon, a cDNA-to-genome alignment program based on a PairHMM probability model. This model relies on prior knowledge, such as the fact that introns must begin with GT, GC, or AT and end with AG or AC. It produces very precise alignments of high quality cDNA sequences. In the genomic regions between Pairagon's cDNA alignments, the pipeline combines EST alignments with de novo gene prediction by using N-SCAN_EST. N-SCAN_EST is based on a generalized HMM probability model augmented with a phylogenetic conservation model and EST alignments. It can predict complete transcripts by extending or merging EST alignments, but it can also predict genes in regions without EST alignments. Because they are based on probability models, both Pairagon and N-SCAN_EST can be trained automatically for new genomes and data sets. RESULTS: On the ENCODE regions of the human genome, Pairagon+N-SCAN_EST was as accurate as any other system tested in the EGASP assessment, including ENSEMBL and ExoGean. CONCLUSION: With sufficient mRNA/EST evidence, genome annotation without trans alignments can compete successfully with systems like ENSEMBL and ExoGean, which use trans alignments

More than 9,000,000 Unique Genes in Human Gut Bacterial Community: Estimating Gene Numbers Inside a Human Body

BACKGROUND: Estimating the number of genes in human genome has been long an important problem in computational biology. With the new conception of considering human as a super-organism, it is also interesting to estimate the number of genes in this human super-organism. PRINCIPAL FINDINGS: We presented our estimation of gene numbers in the human gut bacterial community, the largest microbial community inside the human super-organism. We got 552,700 unique genes from 202 complete human gut bacteria genomes. Then, a novel gene counting model was built to check the total number of genes by combining culture-independent sequence data and those complete genomes. 16S rRNAs were used to construct a three-level tree and different counting methods were introduced for the three levels: strain-to-species, species-to-genus, and genus-and-up. The model estimates that the total number of genes is about 9,000,000 after those with identity percentage of 97% or up were merged. CONCLUSION: By combining completed genomes currently available and culture-independent sequencing data, we built a model to estimate the number of genes in human gut bacterial community. The total number of genes is estimated to be about 9 million. Although this number is huge, we believe it is underestimated. This is an initial step to tackle this gene counting problem for the human super-organism. It will still be an open problem in the near future. The list of genomes used in this paper can be found in the supplementary table

MetaBinG: Using GPUs to Accelerate Metagenomic Sequence Classification

Metagenomic sequence classification is a procedure to assign sequences to their source genomes. It is one of the important steps for metagenomic sequence data analysis. Although many methods exist, classification of high-throughput metagenomic sequence data in a limited time is still a challenge. We present here an ultra-fast metagenomic sequence classification system (MetaBinG) using graphic processing units (GPUs). The accuracy of MetaBinG is comparable to the best existing systems and it can classify a million of 454 reads within five minutes, which is more than 2 orders of magnitude faster than existing systems. MetaBinG is publicly available at http://cbb.sjtu.edu.cn/~ccwei/pub/software/MetaBinG/MetaBinG.php

The combination approach of SVM and ECOC for powerful identification and classification of transcription factor

<p>Abstract</p> <p>Background</p> <p>Transcription factors (TFs) are core functional proteins which play important roles in gene expression control, and they are key factors for gene regulation network construction. Traditionally, they were identified and classified through experimental approaches. In order to save time and reduce costs, many computational methods have been developed to identify TFs from new proteins and to classify the resulted TFs. Though these methods have facilitated screening of TFs to some extent, low accuracy is still a common problem. With the fast growing number of new proteins, more precise algorithms for identifying TFs from new proteins and classifying the consequent TFs are in a high demand.</p> <p>Results</p> <p>The support vector machine (SVM) algorithm was utilized to construct an automatic detector for TF identification, where protein domains and functional sites were employed as feature vectors. Error-correcting output coding (ECOC) algorithm, which was originated from information and communication engineering fields, was introduced to combine with support vector machine (SVM) methodology for TF classification. The overall success rates of identification and classification achieved 88.22% and 97.83% respectively. Finally, a web site was constructed to let users access our tools (see Availability and requirements section for URL).</p> <p>Conclusion</p> <p>The SVM method was a valid and stable means for TFs identification with protein domains and functional sites as feature vectors. Error-correcting output coding (ECOC) algorithm is a powerful method for multi-class classification problem. When combined with SVM method, it can remarkably increase the accuracy of TF classification using protein domains and functional sites as feature vectors. In addition, our work implied that ECOC algorithm may succeed in a broad range of applications in biological data mining.</p