What is worse than getting your paper rejected? It is to get the paper published in late December, when everyone is away for vacation. The TRAPID paper by Michiel van Bel and colleagues had such misfortune, but we believe the labs trying to annotate and analyze RNAseq data will find it helpful. The groups of Yves Van de Peer and Klaas Vandepoele published many other useful bioinformatics tools in the past.
Transcriptome analysis through next-generation sequencing technologies allows the generation of detailed gene catalogs for non-model species, at the cost of new challenges with regards to computational requirements and bioinformatics expertise. Here, we present TRAPID, an online tool for the fast and efficient processing of assembled RNA-Seq transcriptome data, developed to mitigate these challenges. TRAPID offers high-throughput open reading frame detection, frameshift correction and includes a functional, comparative and phylogenetic toolbox, making use of 175 reference proteomes. Benchmarking and comparison against state-of-the-art transcript analysis tools reveals the efficiency and unique features of the TRAPID system. TRAPID is freely available at http://bioinformatics.psb.ugent.be/webtools/trapid/
We have been studying their implementation in detail over the last week and find many attractive features. It is designed with efficiency in mind.
(ii) Gene search is done using RAPSearch2 instead of NCBI NR BLAST or Interproscan.
In a previous work, we developed RAPSearch, an algorithm that achieved a ~20-90-fold speedup relative to BLAST while still achieving similar levels of sensitivity for short protein fragments derived from NGS data. RAPSearch, however, requires a substantial memory footprint to identify alignment seeds, due to its use of a suffix array data structure. Here we present RAPSearch2, a new memory-efficient implementation of the RAPSearch algorithm that uses a collision-free hash table to index a similarity search database. The utilization of an optimized data structure further speeds up the similarity search-another 2-3 times. We also implemented multi-threading in RAPSearch2, and the multi-thread modes achieve significant acceleration (e.g. 3.5X for 4-thread mode). RAPSearch2 requires up to 2G memory when running in single thread mode, or up to 3.5G memory when running in 4-thread mode.
(iii) Global alignment is done using MUSCLE and tree construction is done by FastTree/PhyML.
(iv) All those packages are integrated in a CakePHP framework and can be downloaded from github.
Schematic overview of the TRAPID pipeline. The TRAPID pipeline consists of two separate steps. The first one is a non-interactive processing step, during which all transcripts are assigned to gene families using a RAPSearch2 similarity search, followed by functional annotation transfer and meta- annotation assignment. The second step is interactive and directly commanded through the website interface. Here, the user has the ability to analyze his data using functional enrichment analyses, multiple sequence alignments, and phylogenetic trees.
Readers may find the following post relevant.
afterPartys main use case scenario is one in which a working biologist has generated a large volume of transcribed sequence data and wishes to turn it into a useful resource that has some durability. By reducing the effort, bioinformatics skills, and computational resources needed to annotate and publish a transcriptome, afterParty will facilitate the annotation and sharing of sequence data that would otherwise remain unavailable. A typical metazoan transcriptome containing several tens of thousands of contigs can be annotated in a few minutes of interactive time and a few days of computational time.