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Smircich, P.

Publications and source records attributed to Smircich, P..

3 recordsLinked to original sources

Translational control by Trypanosoma brucei DRBD18 contributes to the maintenance of the procyclic state

Trypanosoma brucei occupies distinct niches throughout its life cycle, within both the mammalian and tsetse fly hosts. The immunological and biochemical complexity and variability of each of these environments require a reshaping of the protein landscape of the parasite both to evade surveillance and face changing metabolic demands. Whereas most well-studied organisms rely on transcriptional control as the main regulator of gene expression, post-transcriptional control mechanisms are particularly important in T. brucei, and these are often mediated by RNA-binding proteins. DRBD18 is a T. brucei RNA-binding protein that interacts with ribosomal proteins and translation factors. Here, we tested a role for DRBD18 in translational control. We show that DRBD18 depletion by RNA interference leads to altered polysomal profiles with a specific depletion of heavy polysomes. Ribosome profiling analysis reveals that 101 transcripts change in translational efficiency (TE) upon DRBD18 depletion: 41 exhibit decreased TE and 60 exhibit increased TE. A further 66 transcripts are buffered, i.e. changes in transcript abundance are compensated by changes in TE such that the total translational output is expected not to change. Proteomic analysis validates these data. In DRBD18-depleted cells, a cohort of transcripts that codes for procyclic form-specific proteins is translationally repressed while, conversely, transcripts that code for bloodstream form- and metacyclic form-specific proteins are translationally enhanced. These data suggest that DRBD18 contributes to the maintenance of the procyclic state through both positive and negative translational control of specific mRNAs.

microbiology↗

Nanopore quality score resolution can be reduced with little effect on downstream analysis

We investigate the effect of quality score information loss on downstream analysis from nanopore sequencing FASTQ files. We polished denovo assemblies for a mock microbial community and a human genome, and we called variants on a human genome. We repeated these experiments using various pipelines, under various coverage level scenarios, and various quality score quantizers. In all cases we found that the quantization of quality scores cause little difference on (or even improves) the results obtained with the original (non-quantized) data. This suggests that the precision that is currently used for nanopore quality scores is unnecessarily high, and motivates the use of lossy compression algorithms for this kind of data. Moreover, we show that even a non-specialized compressor, like gzip, yields large storage space savings after quantization of quality scores.

bioinformatics↗

RENANO: a REference-based compressor for NANOpore FASTQ files

Nanopore sequencing technologies are rapidly gaining popularity, in part, due to the massive amounts of genomic data they produce in short periods of time (up to 8.5 TB of data in less than 72 hours). In order to reduce the costs of transmission and storage, efficient compression methods for this type of data are needed. Unlike short-read technologies, nanopore sequencing generates long noisy reads of variable length. In this note we introduce RENANO, a reference-based lossless FASTQ data compressor, specifically tailored to compress FASTQ files generated with nanopore sequencing technologies. RENANO builds on the recent compressor ENANO, which is the current state of the art. RENANO focuses on improving the compression of the base call sequence portion of the FASTQ file, leaving the other parts of ENANO intact. Two novel reference-based compression algorithms are introduced, contemplating different scenarios: in the first scenario, a reference genome is available without cost to both the compressor and the decompressor; in the second, the reference genome is available only on the compressor side, and a compacted version of the reference is transmitted to the decompressor as part of the compressed file. To evaluate the proposed algorithms, we compare RENANO against ENANO on several publicly available nanopore datasets. In the first scenario considered, RENANO improves the base call sequences compression of ENANO by 39.8%, on average, over all the datasets. As for total compression (including the other parts of the FASTQ file), the average improvement is 12.7%. In the second scenario considered, the base call compression improvements of RENANO over ENANO range from 15.2% to 49.0%, depending on the coverage of the compressed dataset, while in terms of total size, the improvements range from 5.1% to 16.5%. Implementations of the proposed algorithms are freely available for download at https://github.com/guilledufort/RENANO.

bioinformatics↗