Mira4 assembly of 454 reads from SRA

I want to make an assembly of the Annona squamosa fruit transcriptome data from this paper (http://dx.doi.org/10.1186/s12864-015-1248-3). They give in the paper a link to a web resource (http://www.annonatranscriptome.nabi.res.in/), but the resource appears to now be defunct, so to get contigs reads, I will have to assemble the reads myself. The reads are from two different cultivars of Annona squamosa, so I'm going to assemble each cultivar separately first, and then if that works, I'll try a combined assembly.

MIRA is a nice, free, software package that can assemble 454 data. I've had success with it before, so that's what I'll use for this project too.

rendering zdock server results using python and UCSF Chimera

Problem:
I was interested in learning where the protein-protein interaction sites were on a particular protein (the receptor). I had pdb files for that protein, and for three proteins that it is known to bind to (the ligands). There are specific amino-acids of interest on the receptor protein, where there is variability among different species. For every combination of receptor and ligand, I want to perform a prediction of where the proteins interact, and then generate an image where the variable amino-acids are highlighted.

Assembling the Mentha Longifolia genome

A group of researchers, including myself, recently made a draft genome assembly for Mentha longifolia. The publication for which is currently under review. This post is my notes for my part of that effort. Basically that means that this is my lab notebook for part of the summer of 2014. It's pretty disorganized and may or may not be useful to anyone ever....


Long story short:
I was trying to figure out how to integrate PacBio reads with Illumina reads.
In the end, I used ECtools to correct the PacBio reads, and then used PBJelly to merge the PacBio reads with an assembly generated from the Illumina reads. It is my understanding that there may be better options for PacBio error correction today, but at the time ECtools seemed to be the best.

Read on for the juicy details....

Annotating a de-Novo transcriptome assembly

Here's how I'm annotating a de-Novo transcriptome assembly:

This post is kind of halfway between a how-to and a lab notebook entry.

I'm on a Linux box with 12 cores (24 if you count hyper threading), 48 GB of RAM, and Xubuntu Desktop 13.10


I'm basically just using the Trinity pipeline. I think it's turning into a really nice set of tools both for assembly and for annotation (because Trinotate is so new and apparently under heavy active development, the information in this guide may not be current for very long). I'm also using Blast2GO, which has been around longer and is very widely used.

I'm trying to annotate 5 transcriptomes simultaneously, so as I'm going, I'm writing little shell scripts to apply each step to all of the transcriptomes at once (sadly, I had a hard-drive crash and lost those scripts... oh well). At some point I might consolidate them into a more fully automated pipeline, but I'm not going to worry about that for now.

Comparing data from open natural products chemical spectral databases

I recently wrote a review article called "Open-access metabolomics databases for natural product research: present capabilities and future potential", for the journal Frontiers in Bioengineering and Biotechnology, section Bioinformatics and Computational Biology (link).

Part of the review involved evaluating the contents of In this post, this post consists of the notes I took when generating those comparisons. The code I used is available from my Bitbucket account (I apologize for the poor organization and near-lack of documentation), some of the scripts depend on srj_chembiolib, and/or Pandas. InChI normalization relies on ChemAxon molconvert.


Factors to compare: how many unique compounds are present in the database, how many structures have the various kinds of associated analytical spectra, what compound classes are represented.