Shark: fishing relevant reads in an RNA-Seq sample

Motivation Recent advances in high-throughput RNA-Seq technologies allow to produce massive datasets. When a study focuses only on a handful of genes, most reads are not relevant and degrade the performance of the tools used to analyze the data. Removing irrelevant reads from the input dataset leads to improved efficiency without compromising the results of the study. Results We introduce a novel computational problem, called gene assignment and we propose an efficient alignment-free approach to solve it. Given an RNA-Seq sample and a panel of genes, a gene assignment consists in extracting from the sample, the reads that most probably were sequenced from those genes. The problem becomes more complicated when the sample exhibits evidence of novel alternative splicing events. We implemented our approach in a tool called Shark and assessed its effectiveness in speeding up differential splicing analysis pipelines. This evaluation shows that Shark is able to significantly improve the performance of RNA-Seq analysis tools without having any impact on the final results. Availability and implementation The tool is distributed as a stand-alone module and the software is freely available at https://github.com/AlgoLab/shark. Contact [email protected] Supplementary information Supplementary data are available at Bioinformatics online.

Shark: fishing in a sample to discard useless RNA-Seq reads

Recent advances in high throughput RNA-Seq technologies allow to produce massive datasets. When a study focuses only on a handful of genes, most reads are not relevant and degrade the performance of the tools used to analyze the data. Removing such useless reads from the input dataset leads to improved efficiency without compromising the results of the study.To this aim, in this paper we introduce a novel computational problem, called gene assignment and we propose an efficient alignment-free approach to solve it. Given a RNA-Seq sample and a panel of genes, a gene assignment consists in extracting from the sample the reads that most probably were sequenced from those genes. The problem becomes more complicated when the sample exhibits evidence of novel alternative splicing events.We implemented our approach in a tool called Shark and assessed its effectiveness in speeding up differential splicing analysis pipelines. This evaluation shows that Shark is able to significantly improve the performance of RNA-Seq analysis tools without having any impact on the final results.The tool is distributed as a stand-alone module and the software is freely available at https://github.com/AlgoLab/shark.

Pollinizer Efficacy of Several ‘Ingeborg’ Pear Pollinizers in Hardanger, Norway, Examined Using Microsatellite Markers

‘Ingeborg’ is currently the main commercial pear cultivar grown in Norway. However, fruit set and subsequent yields of this cultivar have proven to be variable and overall low averaging 10–20 t·ha−1. Pear seeds found in ‘Ingeborg’ fruits are often underdeveloped, suggesting that incomplete fertilization might be a major cause of poor fruit set. In some years, sporadically unfavorable environmental conditions during and immediately after pollination in Hardanger district, western Norway, have resulted in poor fruit set of ‘Ingeborg’. In this study, the pollinizer efficacy of several pollinizers, namely ‘Clara Frijs’, ‘Herzogin Elsa’, ‘Anna’, ‘Colorée de Juillet’, and ‘Belle lucrative’, from several orchards located in the Hardanger district was investigated using 12 microsatellite markers for two growing seasons (2014 and 2016). Pollinizer efficacy was estimated by genotyping ‘Ingeborg’, each individual pollinizer, as well as normally developed seeds from ‘Ingeborg’ fruit, and conducting gene assignment analyses to identify the pollen contribution from each of the pollinizer cultivars. In addition, S-allele genotyping was conducted, and only one pollinizer, ‘Anna’, was identified as being semicompatible with ‘Ingeborg’, whereas all other pollinizers were fully compatible. ‘Clara Frijs’ and ‘Belle lucrative’ were identified as the most efficient pollinizers probably because these cultivars were abundant compared with all other pollinizers within all, but one of the examined orchards. Higher yields could not be attributed to a particular pollinizer, and genetic effects associated with the triploid nature of ‘Ingeborg’ are most likely implicated as a cause behind the low and variable yield of this cultivar.