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PACRAT

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

Analysis of intergenic sequences for purposes such as the investigation of transcriptional signals or the identification of small RNA genes is frequently complicated by traditional biological database structures. Genome data is commonly treated as chromosome-length sequence records, detailed by gene calls demarcating subsequences of the chromosomes. Given this model, the determination of non-called subsequences between any gene and its nearest neighbors requires an exhaustive search of all gene calls associated with the chromosome.

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New support vector machine-based method for microRNA target prediction

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

MicroRNA (miRNA) plays important roles in cell differentiation, proliferation, growth, mobility, and apoptosis. An accurate list of precise target genes is necessary in order to fully understand the importance of miRNAs in animal development and disease. Several computational methods have been proposed for miRNA target-gene identification. However, these methods still have limitations with respect to their sensitivity and accuracy. Thus, we developed a new miRNA target-prediction method based on the support vector machine (SVM) model.

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dbPHCC

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

Hepatocellular carcinoma (HCC) is one of the most common malignant cancers with a poor prognosis. For decades, more and more biomarkers were found to effect on HCC prognosis, but these studies were scattered and there were no unified identifiers. Therefore, we built the database of prognostic biomarkers and models for hepatocellular carcinoma (dbPHCC).

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MIRAGAA

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

Cancer evolves through microevolution where random lesions that provide the biggest advantage to cancer stand out in their frequent occurrence in multiple samples. At the same time, a gene function can be changed by aberration of the corresponding gene or modification of microRNA (miRNA) expression, which attenuates the gene. In a large number of cancer samples, these two mechanisms might be distributed in a coordinated and almost mutually exclusive manner.

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MiRE

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

To provide a set of useful analysis tools for the researchers to explore the microRNA data.
The R language was used for generating the Graphical Users Interface and implementing most functions. Some Practical Extraction and Report Language (Perl) scripts were used for parsing source files.

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ESPSearch

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

ESPSearch is a computer program for rapidly identifying nucleic acid or amino acid sequences of any length within any source sequence from promoters to entire genomes to protein libraries. ESPSearch utilizes a user-constructed database to identify many sequences simultaneously, including target sequences with wildcards and mismatches and user-specified patterns of those recognized sequences.

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miRTP

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

We used a machine learning method, the nearest neighbor algorithm (NNA), to learn the relationship between miRNAs and their target proteins, generating a predictor which can then judge whether a new miRNA-target pair is true or not. We acquired 198 positive (true) miRNA-target pairs from Tarbase and the literature, and generated 4,888 negative (false) pairs through random combination. A 0/1 system and the frequencies of single nucleotides and di-nucleotides were used to encode miRNAs into vectors while various physicochemical parameters were used to encode the targets.

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RespCanDB

Submitted by ChenLiang on Sun, 09/10/2017 - 20:13

Respiratory cancer database (RespCanDB) is a genomic and proteomic database of cancer of respiratory organ. It also includes the information of medicinal plants used for the treatment of various respiratory cancers with structure of its active constituents as well as pharmacological and chemical information of drug associated with various respiratory cancers.

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JBCB

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

Current miRNA target prediction tools have the common problem that their false positive rate is high. This renders identification of co-regulating groups of miRNAs and target genes unreliable. In this study, we describe a procedure to identify highly probable co-regulating miRNAs and the corresponding co-regulated gene groups.

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siRNAs with high specificity to the target

Submitted by ChenLiang on Fri, 09/02/2016 - 21:59

'Off-target' silencing effect hinders the development of siRNA-based therapeutic and research applications. Common solution to this problem is an employment of the BLAST that may miss significant alignments or an exhaustive Smith-Waterman algorithm that is very time-consuming. We have developed a Comprehensive Redundancy Minimizer (CRM) approach for mapping all unique sequences ("targets") 9-to-15 nt in size within large sets of sequences (e.g. transcriptomes). CRM outputs a list of potential siRNA candidates for every transcript of the particular species.

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