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12
Tools for mapping high-throughput sequencing data
- Bioinformatics
, 2012
"... Motivation: A ubiquitous and fundamental step in high-throughput sequencing analysis is the alignment (mapping) of the generated reads to a reference sequence. To accomplish this task numerous software tools have been proposed. Determining the mappers that are most suitable for a specic application ..."
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Motivation: A ubiquitous and fundamental step in high-throughput sequencing analysis is the alignment (mapping) of the generated reads to a reference sequence. To accomplish this task numerous software tools have been proposed. Determining the mappers that are most suitable for a specic application is not trivial. Results: This survey focuses on classifying mappers through a wide number of characteristics. The goal is to allow practitioners to com-pare the mappers more easily and nd those that are most suitable for their specic problem. Availability: A regularly updated compendium of mappers can be found at
Translational biomedical informatics in the cloud: present and future
- 2013, Article ID 658925
"... Next generation sequencing and other high-throughput experimental techniques of recent decades have driven the exponential growth in publicly available molecular and clinical data. This information explosion has prepared the ground for the development of translational bioinformatics. The scale and ..."
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Next generation sequencing and other high-throughput experimental techniques of recent decades have driven the exponential growth in publicly available molecular and clinical data. This information explosion has prepared the ground for the development of translational bioinformatics. The scale and dimensionality of data, however, pose obvious challenges in data mining, storage, and integration. In this paper we demonstrated the utility and promise of cloud computing for tackling the big data problems. We also outline our vision that cloud computing could be an enabling tool to facilitate translational bioinformatics research.
B: Translational bioinformatics for diagnostic and prognostic prediction of prostate cancer in the nextgeneration sequencing era. BioMed research international 2013
"... The discovery of prostate cancer biomarkers has been boosted by the advent of next-generation sequencing (NGS) technologies. Nevertheless, many challenges still exist in exploiting the flood of sequence data and translating them into routine diagnostics and prognosis of prostate cancer. Here we rev ..."
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The discovery of prostate cancer biomarkers has been boosted by the advent of next-generation sequencing (NGS) technologies. Nevertheless, many challenges still exist in exploiting the flood of sequence data and translating them into routine diagnostics and prognosis of prostate cancer. Here we review the recent developments in prostate cancer biomarkers by high throughput sequencing technologies. We highlight some fundamental issues of translational bioinformatics and the potential use of cloud computing in NGS data processing for the improvement of prostate cancer treatment.
Grape RNA-Seq analysis pipeline environment
"... Motivation: The avalanche of data arriving since the development of next generation sequencing (NGS) technologies has prompted the need for developing fast, accurate, and easily automated bioinforma-tic tools capable of dealing with massive datasets. Among the most productive applications of NGS tec ..."
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Motivation: The avalanche of data arriving since the development of next generation sequencing (NGS) technologies has prompted the need for developing fast, accurate, and easily automated bioinforma-tic tools capable of dealing with massive datasets. Among the most productive applications of NGS technologies is the sequencing of cel-lular RNA, known as RNA-Seq. While RNA-Seq provides similar or superior dynamic range than microarrays at similar or lower cost, the lack of standard and user-friendly pipelines is a bottleneck preventing RNA-Seq from becoming the standard for transcriptome analysis. Results: In this work we present a pipeline for processing and analyzing RNA-Seq data, that we have named Grape (Grape RNA-Seq Analysis Pipeline Environment). Grape supports raw sequencing reads produced by a variety of technologies, either in Fasta or FastQ format, or as prealigned reads in SAM/BAM format. A minimal Grape conguration consists of the le location of the raw sequencing reads, the genome of the species, and the corresponding gene and transcript annotation. Grape rst runs a set of quality control (QC) steps, and then aligns the reads to the genome, a step that is omitted for prealigned read formats. Grape next estimates gene and transcript expression levels, calculates exon inclusion levels, and identies novel transcripts. Grape can be run on a single computer or in parallel on a computer cluster. It is distributed with specic mapping and quantication tools, but given its modular design, any tool supporting popular data inter-change formats, can be integrated. Availability: Grape can be obtained from the Bioinformatics and
Review Article Translational Biomedical Informatics in the Cloud:
, 2013
"... which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Next generation sequencing and other high-throughput experimental techniques of recent decades have driven the exponential growth in publicly available molecular and clinical d ..."
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which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Next generation sequencing and other high-throughput experimental techniques of recent decades have driven the exponential growth in publicly available molecular and clinical data. This information explosion has prepared the ground for the development of translational bioinformatics.The scale and dimensionality of data, however, pose obvious challenges in data mining, storage, and integration. In this paper we demonstrated the utility and promise of cloud computing for tackling the big data problems. We also outline our vision that cloud computing could be an enabling tool to facilitate translational bioinformatics research. 1.
Analysis of High Throughput Genomic Datasets Across Species
, 2012
"... official polices, either expressed or implied, or any sponsoring institution, the U.S. government, or any other entity. Keywords: dynamic network analysis, gene expression analysis, cross species analysis, protein-protein interactions, genetic interactions, soft clustering, active sub-networks, cons ..."
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official polices, either expressed or implied, or any sponsoring institution, the U.S. government, or any other entity. Keywords: dynamic network analysis, gene expression analysis, cross species analysis, protein-protein interactions, genetic interactions, soft clustering, active sub-networks, conservation and divergenceThis dissertation is dedicated to my parents and friends Genes are highly conserved between closely related species, and biological systems often utilize the same genes across different organisms. This fact has allowed the study of various biological systems using model organisms and the development of many drugs for human diseases by first researching simpler model organisms. New high-throughput technologies have enabled researchers to use interactions and expression data to get a more precise view regarding the roles and functions of biological processes across species. However, combining and comparing these types of data across species is challenging due to several problems including homology assignments, coverage issues, and quality of the data in each of the species. This thesis studies various aspects of cross species analysis in light of these obstacles and introduces new algorithms and computational tools that specifically address them. First, we performed a global analysis of conservation of interaction and expression data by developing a framework that integrated various data types from four model organisms. This analysis showed
l- Current address:
, 2012
"... Applications of next-generation sequencing in fish ..."
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Review Article Enabling Large-Scale Biomedical Analysis in the Cloud
, 2013
"... Copyright © 2013 Ying-Chih Lin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Recent progress in high-throughput instrumentat ..."
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Copyright © 2013 Ying-Chih Lin et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. Recent progress in high-throughput instrumentations has led to an astonishing growth in both volume and complexity of biomedical data collected from various sources. The planet-size data brings serious challenges to the storage and computing technologies. Cloud computing is an alternative to crack the nut because it gives concurrent consideration to enable storage and high-performance computing on large-scale data.Thiswork briefly introduces the data intensive computing systemand summarizes existing cloud-based resources in bioinformatics.These developments and applicationswould facilitate biomedical research tomake the vast amount of diversification data meaningful and usable. 1.
