Showing results for: [ Papanicolaou, Alexie ]
The aim of the project was to generate a high-quality reference transcriptome for Murray-Darling rainbowfish to enable the design of genome-wide expression analysis strategies (such as microarrays) an... mored to obtain protein-coding sequences for nuclear receptors and steroid biosynthetic enzymes involved in sexual development and reproduction. Assembled transcripts, predicted coding regions, deduced proteins and tentative descriptions are available in this collection.less
CLSD-Molecular biomarkers in rainbow - RNA-Seq - Published 06 Nov 2015
The DEW pipeline is a one command line solution that aligns reads, corrects expression biases (eXpress), TMM normalizes (edgeR) and produces FPKM values that are then visualized with pretty ggplot gra... morephics.
This BZ2-compressed tar archive provides the software and licensing statements. DEW is written in Perl and requires a SQLite database and is supported for Linux 64-bit computers. The homepage of the software can be found at http://dew.sourceforge.net.less
Cont to TCP01 Dissecting Adaptive - transcriptomics software development - Published 06 Jan 2015
This our GeoGenetics prototype on phylogeography and genetics to use a large array of data. Using Chado (GMOD.org) and web-services we have built most of the tool allowing for an interaction of geo-lo... morecated samples with an unlimited array of data types. The data can be hosted locally or from any other server that serves the GeoJSON format.
An added capability is the intersection of these data with more than 200 environmental layers derived from the Atlas of Living Australia.
A further prototype capability is the ability to search for RNAi matches, as developed for a USDA proposal called "Reducing off-target effects of RNAi pesticides via functional genomics".
Cont to TCP01 Dissecting Adaptive - GeoGenetics software development - Published 06 Jan 2015
An increased number of genomes are being made public but few individual research are willing to take ownership of their own data. Indeed, the current model is for genome sequences to be handled by seq... moreuencing centers or large bioinformatic repositories (RefSeq or Ensembl). Even though using these widely used and standardized repositories and center is an excellent model to decrease the cost of completing a genome project, this comes at a cost. First, these groups have in-house pipelines built and customized for the projects that financially support them (i.e. small genomes of microbial human pathogens and largely fully complete genomes such as Drosophila or human) rather than say a highly polymorphic species from a natural ecosystem. Second, the lack of a robust funding model means that these repositories are do not have the resources to offer community-wide support and customization of a pipeline. Third, and perhaps most importantly, these centers and repositories usually lack the domain expertise associated with the biology of the species.
For these (and perhaps others?) reasons, genome consortia that have access to genomicists (or PhD students and post-docs willing to learn) are either collaborating with bioinformatic laboratories or investing in their own annotation capability. This endeveavour has been greatly helped by the public availability of the tools used by the repositories and sequencing centers (e.g. GMOD, Ensembl and sequencing-center specific platforms such as those from the Broad Institute). The GMOD project specifically specializes in compact, user friendly solutions that just_work. For example, MAKER requires a few minutes of configuration to deliver a standardized annotation for gene models. At the other side of the spectrum, Ensembl delivers a comprehensive solution, database and informatic pipelines that - in the hands of a highly-trained bioinformatician - can deliver the same depth and level of annotation as that used by the EBI. There really is no a solution that fits in-between. There is also almost no software that also wishes to educate the user rather than offering a black box. Finally, there is no solution that we know of that can also functionally annotate the genome (a la BLAST2GO but free) and then link the concept of gene model (feature) annotation with functional annotation.
The JAMg software was created to address the issue of creating gene models (feature annotation) and was built by Alexie Papanicolaou at the Commonwealth Scientific and Industrial Research Organisation (CSIRO) with some brilliant support from Brian Haas at the Broad Institute. The software and manual are written so that to guide the annotation process so that users can follow the process closely. Even though with JAMg you will not need another genome annotation pipeline, JAMg does not aim to replace other genome annotation pipelines (e.g. each sequencing center has its own): it does aim to support nascent genome annotators and (ultimately) educate its users about genome annotation in general. As part of our Just_Annotate series, JAMg links with JAMp and WebApollo to provide a first solution for users wishing to go from genome assembly to deriving biological hypotheses.
This BZ2-compressed tar archive provides the software, HTML documentation and licensing statements. The expanded folder tree requires at least 2.5 GB of disk space. JAMg software is written in Perl and is supported for Linux 64-bit computers. See the homepage at http://jamg.sourceforge.net for new versions.
Cont to TCP01 Dissecting Adaptive - genome annotation software development - Published 06 Jan 2015
Whether working on a model or non-model species for biomedical, economical or evolutionary research, next-generation sequencing has enabled biologists to rapidly generate a reference sequence for down... morestream applications and hypotheses generation. With the exception of a limited number of species, functional annotation is conducted by in-silico experiments based on sequence similarity. Some groups are also enriching their data with expression studies. JAMp is a platform that allows biologists to reclaim the analysis of transcript reconstruction experiments by providing an automated process for generating functional annotations and a user-friendly overview of these in-silico experiments. The entire software is built so that novice command-line users can take a transcriptome assembly and generate websites like those found in our demo.
Supplementary data for the BMC Genomics paper titled: "Identification and Characterization of Three Chemosensory Receptor Families in the Cotton Bollworm Helicoverpa armigera"
Catalytic Dissecting Adaptive Potential - Phlyogenetics analysis - Published 05 Jun 2014