Finding Function in the Unknown

Kelly Boyd; Emma Highland; Amanda Misch; Amber Hu; Sushma Reddy; Catherine Putonti

doi:10.1109/BIBM.2015.7359834

Finding Function in the Unknown

Kelly Boyd, Emma Highland, Amanda Misch, Amber Hu, Sushma Reddy, Catherine Putonti

Research output: Contribution to journal › Article › peer-review

Abstract

Through high-throughput RNA sequencing (RNAseq), transcriptomes for a single cell, tissue, or organism(s) can be ascertained at a high resolution. While a number of bioinformatic tools have been developed for transcriptome analyses, significant challenges exist for studies of non-model organisms. Without a reference sequence available, raw reads must first be assembled de novo followed by the tedious task of BLAST searches and data mining for functional information. We have created a pipeline, PyRanger, to automate this process. The pipeline includes functionality to assess a single transcriptome and also facilitate comparative transcriptomic studies

Original language	American English
Journal	2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)
DOIs	https://doi.org/10.1109/BIBM.2015.7359834
State	Published - Dec 17 2015

Keywords

transcriptomics
RNAseq
bioinformatics
computer science
non-model organisms
de novo transcriptomics
RNA-seq
comparative gene expression

Disciplines

Bioinformatics
Computational Biology
Genomics

Access to Document

10.1109/BIBM.2015.7359834License: Unspecified

Cite this

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title = "Finding Function in the Unknown",

abstract = " Through high-throughput RNA sequencing (RNAseq), transcriptomes for a single cell, tissue, or organism(s) can be ascertained at a high resolution. While a number of bioinformatic tools have been developed for transcriptome analyses, significant challenges exist for studies of non-model organisms. Without a reference sequence available, raw reads must first be assembled de novo followed by the tedious task of BLAST searches and data mining for functional information. We have created a pipeline, PyRanger, to automate this process. The pipeline includes functionality to assess a single transcriptome and also facilitate comparative transcriptomic studies ",

keywords = "transcriptomics, RNAseq, bioinformatics, computer science, non-model organisms, de novo transcriptomics, RNA-seq, comparative gene expression",

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AU - Boyd, Kelly

AU - Highland, Emma

AU - Misch, Amanda

AU - Hu, Amber

AU - Reddy, Sushma

AU - Putonti, Catherine

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PY - 2015/12/17

Y1 - 2015/12/17

N2 - Through high-throughput RNA sequencing (RNAseq), transcriptomes for a single cell, tissue, or organism(s) can be ascertained at a high resolution. While a number of bioinformatic tools have been developed for transcriptome analyses, significant challenges exist for studies of non-model organisms. Without a reference sequence available, raw reads must first be assembled de novo followed by the tedious task of BLAST searches and data mining for functional information. We have created a pipeline, PyRanger, to automate this process. The pipeline includes functionality to assess a single transcriptome and also facilitate comparative transcriptomic studies

AB - Through high-throughput RNA sequencing (RNAseq), transcriptomes for a single cell, tissue, or organism(s) can be ascertained at a high resolution. While a number of bioinformatic tools have been developed for transcriptome analyses, significant challenges exist for studies of non-model organisms. Without a reference sequence available, raw reads must first be assembled de novo followed by the tedious task of BLAST searches and data mining for functional information. We have created a pipeline, PyRanger, to automate this process. The pipeline includes functionality to assess a single transcriptome and also facilitate comparative transcriptomic studies

KW - transcriptomics

KW - RNAseq

KW - bioinformatics

KW - computer science

KW - non-model organisms

KW - de novo transcriptomics

KW - RNA-seq

KW - comparative gene expression

UR - https://ieeexplore.ieee.org/document/7359834

UR - https://ecommons.luc.edu/bioinformatics_facpub/27

U2 - 10.1109/BIBM.2015.7359834

DO - 10.1109/BIBM.2015.7359834

M3 - Article

JO - 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

JF - 2015 IEEE International Conference on Bioinformatics and Biomedicine (BIBM)

ER -

Finding Function in the Unknown

Abstract

Keywords

Disciplines

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