An effective approach for identification of <Emphasis Type="Italic">in vivo</Emphasis> protein-DNA binding sites from paired-end ChIP-Seq data

Background  

ChIP-Seq, which combines chromatin immunoprecipitation (ChIP) with high-throughput massively parallel sequencing, is increasingly being used for identification of protein-DNA interactions in vivo in the genome. However, to maximize the effectiveness of data analysis of such sequences requires the development of new algorithms that are able to accurately predict DNA-protein binding sites.     

In vivo studies of genomic packaging in the dsRNA bacteriophage Φ8

Background  

Φ8 is a bacteriophage containing a genome of three segments of double-stranded RNA inside a polyhedral capsid enveloped in a lipid-containing membrane. Plus strand RNA binds and is packaged by empty procapsids. Whereas Φ6, another member of the Cystoviridae, shows high stringency, serial dependence and precision in its genomic packaging in vitro and in vivo, Φ8 packaging is more flexible. Unique sequences (pac) near the 5' ends of plus strands are necessary and sufficient for Φ6 genomic packaging and the RNA binding sites are located on P1, the major structural protein of the procapsid.     

PSSM-based prediction of DNA binding sites in proteins

Background  

Detection of DNA-binding sites in proteins is of enormous interest for technologies targeting gene regulation and manipulation. We have previously shown that a residue and its sequence neighbor information can be used to predict DNA-binding candidates in a protein sequence. This sequence-based prediction method is applicable even if no sequence homology with a previously known DNA-binding protein is observed. Here we implement a neural network based algorithm to utilize evolutionary information of amino acid sequences in terms of their position specific scoring matrices (PSSMs) for a better prediction of DNA-binding sites.     

G-InforBIO: integrated system for microbial genomics

Background  

Genome databases contain diverse kinds of information, including gene annotations and nucleotide and amino acid sequences. It is not easy to integrate such information for genomic study. There are few tools for integrated analyses of genomic data, therefore, we developed software that enables users to handle, manipulate, and analyze genome data with a variety of sequence analysis programs.     

Ori-Finder: A web-based system for finding <Emphasis Type="Italic">oriC</Emphasis> s in unannotated bacterial genomes

Background  

Chromosomal replication is the central event in the bacterial cell cycle. Identification of replication origins (oriCs) is necessary for almost all newly sequenced bacterial genomes. Given the increasing pace of genome sequencing, the current available software for predicting oriCs, however, still leaves much to be desired. Therefore, the increasing availability of genome sequences calls for improved software to identify oriCs in newly sequenced and unannotated bacterial genomes.     

Optimizing amino acid substitution matrices with a local alignment kernel

Background  

Detecting remote homologies by direct comparison of protein sequences remains a challenging task. We had previously developed a similarity score between sequences, called a local alignment kernel, that exhibits good performance for this task in combination with a support vector machine. The local alignment kernel depends on an amino acid substitution matrix. Since commonly used BLOSUM or PAM matrices for scoring amino acid matches have been optimized to be used in combination with the Smith-Waterman algorithm, the matrices optimal for the local alignment kernel can be different.     

Transposable element derived DNaseI-hypersensitive sites in the human genome

Background  

Transposable elements (TEs) are abundant genomic sequences that have been found to contribute to genome evolution in unexpected ways. Here, we characterize the evolutionary and functional characteristics of TE-derived human genome regulatory sequences uncovered by the high throughput mapping of DNaseI-hypersensitive (HS) sites.