bims-micpro Biomed News
on Discovery and characterization of microproteins
Issue of 2020–11–22
two papers selected by
Thomas Farid Martínez, University of California, Irvine
  1. Stochastic Gain and Loss of Novel Transcribed Open Reading Frames in the Human Lineage.
  2. OCCAM: prediction of small ORFs in bacterial genomes by means of a target-decoy database approach and machine learning techniques.
  1. Genome Biol Evol. 2020 Nov 03. 12(11): 2183-2195
    Stochastic Gain and Loss of Novel Transcribed Open Reading Frames in the Human Lineage.
    Daniel Dowling, Jonathan F Schmitz, Erich Bornberg-Bauer.
      In addition to known genes, much of the human genome is transcribed into RNA. Chance formation of novel open reading frames (ORFs) can lead to the translation of myriad new proteins. Some of these ORFs may yield advantageous adaptive de novo proteins. However, widespread translation of noncoding DNA can also produce hazardous protein molecules, which can misfold and/or form toxic aggregates. The dynamics of how de novo proteins emerge from potentially toxic raw materials and what influences their long-term survival are unknown. Here, using transcriptomic data from human and five other primates, we generate a set of transcribed human ORFs at six conservation levels to investigate which properties influence the early emergence and long-term retention of these expressed ORFs. As these taxa diverged from each other relatively recently, we present a fine scale view of the evolution of novel sequences over recent evolutionary time. We find that novel human-restricted ORFs are preferentially located on GC-rich gene-dense chromosomes, suggesting their retention is linked to pre-existing genes. Sequence properties such as intrinsic structural disorder and aggregation propensity-which have been proposed to play a role in survival of de novo genes-remain unchanged over time. Even very young sequences code for proteins with low aggregation propensities, suggesting that genomic regions with many novel transcribed ORFs are concomitantly less likely to produce ORFs which code for harmful toxic proteins. Our data indicate that the survival of these novel ORFs is largely stochastic rather than shaped by selection.
    Keywords:  de novo genes; novel genes; orphan genes; primate genomics; small ORFs; small proteins
    DOI:  https://doi.org/10.1093/gbe/evaa194
  2. Database (Oxford). 2020 Jan 01. pii: baaa067. [Epub ahead of print]2020
    OCCAM: prediction of small ORFs in bacterial genomes by means of a target-decoy database approach and machine learning techniques.
    Fabio R Cerqueira, Ana Tereza Ribeiro Vasconcelos.
      Small open reading frames (ORFs) have been systematically disregarded by automatic genome annotation. The difficulty in finding patterns in tiny sequences is the main reason that makes small ORFs to be overlooked by computational procedures. However, advances in experimental methods show that small proteins can play vital roles in cellular activities. Hence, it is urgent to make progress in the development of computational approaches to speed up the identification of potential small ORFs. In this work, our focus is on bacterial genomes. We improve a previous approach to identify small ORFs in bacteria. Our method uses machine learning techniques and decoy subject sequences to filter out spurious ORF alignments. We show that an advanced multivariate analysis can be more effective in terms of sensitivity than applying the simplistic and widely used e-value cutoff. This is particularly important in the case of small ORFs for which alignments present higher e-values than usual. Experiments with control datasets show that the machine learning algorithms used in our method to curate significant alignments can achieve average sensitivity and specificity of 97.06% and 99.61%, respectively. Therefore, an important step is provided here toward the construction of more accurate computational tools for the identification of small ORFs in bacteria.
    DOI:  https://doi.org/10.1093/database/baaa067
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