Category Archives: Posters

Algolab at Recomb 2018

RECOMB 2018 is the 22nd edition of a series of well-established scientific conferences bridging the areas of computational, mathematical, statistical and biological sciences.  The 2018 edition was held in  Paris from April, 21st through 24th of 2018, and was preceded by a series of satellite workshops on the 19th-20th of April.

Luca Denti and Murray Patterson presented posters at the main conference titled:

  • ASGAL: Aligning RNA-Seq Data to a Splicing Graph to Detect Novel Alternative Splicing Events which won best poster award (link to this poster, and preprint), and
  • HapCHAT: Adaptive haplotype assembly for efficiently leveraging high coverage in long reads (link to this poster, and preprint).

Simone Ciccolella and Murray Patterson also presented a talk, as well as a poster at the Recomb-CCB satellite meeting of Recomb, both titled:

  • Inferring Cancer Progression from Single Cell Sequencing while allowing loss of mutations

for which a preprint can be found here.

An in-silico framework for comparing and validating transcripts predicted from single and paired-end reads

Anna Paola Carrieri, Stefano Beretta, Gianluca Della Vedova, Ernesto Picardi, Yuri Pirola, Raffaella Rizzi, Graziano Pesole, and Paola Bonizzoni, NGS 2012 (poster and abstract).

With the advent of high-throughput sequencing of transcriptome (RNA-Seq), different computational methods that use RNA-Seq data to assemble full-length mRNA isoforms have been proposed, albeit not solving completely the problem. We have analyzed some of the most used available tools, evaluating their performance and accuracy.

Our experimental analysis reveals that using GSNAP instead of TopHat gives more specific predictions with also a minor (but statistically inconclusive) improvement in sensitivity.

We plan to extend our study (i) by introducing some alternatives to EVAL for comparing predictions, (ii) by considering different kinds of simulated data (more coverage levels and/or errors), as well as real data, and (iii) by analyzing in more detail the structure of predicted transcripts since a preliminary study in this direction reveals that the actual methods have various shortcomings in assembling transcripts.