Mar

7

New tools for assembly and alignment – with or without a reference genome

Filed Under Transcriptome Assembly Tools 

With a Genome

FANSe1 is a new, fast and accurate algorithm for nucleic acid sequence analysis with adjustable mismatch allowance settings and ability to handle indels to accurately and quantitatively map millions of reads to small or large reference genomes. It is a seed-based algorithm which uses the whole read information for mapping and high sensitivity and low ambiguity are achieved by using short and non-overlapping reads. Furthermore, FANSe uses hotspot score to prioritize the processing of highly possible matches and implements modified Smith-Watermann refinement with reduced scoring matrix to accelerate the calculation without compromising its sensitivity. The FANSe algorithm stably processes datasets from various sequencing platforms, masked or unmasked and small or large genomes. It shows a remarkable coverage of low-abundance mRNAs which is important for quantitative processing of RNA-Seq datasets.

AVAILABILITY: The FANSe algorithm is accessible at http://bioinformatics.jnu.edu.cn/software/fanse/. The web site contains a detailed tutorial and the source code for download

Without a Genome

Oases2 is a software package designed to heuristically assemble RNA-seq reads in the absence of a reference genome, across a broad spectrum of expression values and in presence of alternative isoforms. It achieves this by using an array of hash lengths, a dynamic filtering of noise, a robust resolution of alternative splicing events, and the efficient merging of multiple assemblies. It was tested on human and mouse RNA-seq data and is shown to improve significantly on the transABySS and Trinity de novo transcriptome assemblers.

AVAILABILITY: Oases is freely available under the GPL license at www.ebi.ac.uk/~zerbino/oases/

  1. Zhang G, Fedyunin I, Kirchner S, Xiao C, Valleriani A, Ignatova Z. (2012) FANSe: an accurate algorithm for quantitative mapping of large scale sequencing reads. Nucleic Acids Res [Epub ahead of print]. [article]
  2. Schulz MH, Zerbino DR, Vingron M, Birney E. (2012) Oases: Robust de novo RNA-seq assembly across the dynamic range of expression levels. Bioinformatics [Epub ahead of print]. [article]

Incoming search terms:

  • transcriptome assembly
  • de novo assembly and analysis of rna-seq data
  • de novo rna-seq assembly
  • rna-seq de novo assembly
  • rna seq analysis without reference genome
  • rna seq data expressiom amalysis wothout refwrwnce genome
  • rna seq data without the genome
  • rna seq genome and transcriptome alignment reference
  • rna seq reference genome
  • rna seq without reference genome

Comments

Leave a Reply




  • Social Networking Pages

    Linkedin Group

  • Follow Me on Pinterest

  • RSS SEQanswers – RNA Sequencing

    • reason for low mapping rate?? May 23, 2013
      we did RNASeq using HiSeq 2000 100PE. When the data were back, I mapping them to the reference sequence, but got very low mapping rate (30-40%). I... […]
      miaom
    • cross-species data - questions about normalization May 23, 2013
      Hi, I have some data form various samples (cell types) in different species. I want to compare and analyze gene expression variability across the... […]
      trelek2
    • CuffDiff strange output May 23, 2013
      Hi, I hope that someone can be so gentle to help me. I'm analizing some data from RNA-Seq with TopHat and Cufflinks and I focus my attention on... […]
      Pruexel
    • cannot away with cuffdiff,incredible May 23, 2013
      Hi,all I have 4(A,B,C,D) sample in 4 times(increasing time),I got diff result in 3 different cuffdiff 1.cuffdiff 3(A,B,C) individual... […]
      upper
    • TopHat extremely low paired mapping rate. PLS HELP! May 22, 2013
      Hey guys, I have some problems with my paried-end RNA seq analysis on Galaxy. As you can see in the bam flagstat output, my tophat alignment rate is... […]
      Felix.Lee
    • Identifying small RNA sequence within whole genome sequence May 21, 2013
      Hi all, I want to know if there are any useful bioinformatic tool to find small RNA sequence within a whole bacteria genome. Thank you in... […]
      Inma

  • RSS Biostar – RNA-Seq

    • Why am I getting so many unmapped reads in STAR, classified as "too short"?
      I am currently using STAR to map several Hi-SEQ mRNA runs. I'm having trouble getting a decent amount of reads to map, but I don't really understand why. I'm hoping you can shed some light :) In the final log, only about 50% (or less) of the reads map to the reference. I'm using a GTF in addition to the genome. The unmapped bin that most […]
    • What are the best practices for SNP identification in RNA seq transcriptome data
      I have 20 RICE RNA seq tranascriptome data hiseq 2000 platform paired end reads. I aligned fasta reads with BWA and remove PCR duplicates with PICARD. Later I call SNP with samtools using various parameters. I would like to clarify what parameters should I used while alinging to reference rice genome for looking SNP location 100 bp upstream and 250 bp downst […]
    • How do TopHat options -g , --supress-hits, and Bowtie options interplay?
      Hi, I am currently using TopHat2 to map RNA-seq runs. I think there have been some changes pertaining the -g option. Does anyone know how it works now? I used to think that setting -g would look for n alignments for a given read, report them [if top-scoring] and discard those reads that had more than g [top scoring] alignments. Now, the description sounds mo […]
    • What happened to -k in TopHat for multiple-mapping reads?
      Selecting -g n in tophat does not discard reads mapping more than n, but instead only reports n alignments for those out all all their TOP scoring alignments. I think there used to be an option -k that would allow one to discard reads that topped x alignments -- whatever happened to that? I only see -g in the tophat 2 manual, no reporting options like before […]
    • Does tophat use the library-type information for mapping, or just for the XS flag?
      When I specify library-type to TopHat, i.e., first-strand, second-strand, unstranded, TopHat appends a value + or - to the XS:A flag, which is useful for subsequent analyses, such as annotation. However, does this information actually influence the "mappability" of reads, or is this unaffected? My thinking is that the information would be considere […]
    • Purpose of Y-shaped adapters in Illumina Sequencing?
      Hi all, Y adapters different sequences to be annealed to the 5' and 3' ends of each molecule in a library. The arms of the Y are unique, and the middle part, connected to the DNA fragment, is complementary. What are the advantages of this? My take of this over having fully-complementary adapters (ADAPTER1 - - - - - ADAPTER1) is that: -Upon primer a […]