Recognizing short coding sequences of prokaryotic genome using a novel iteratively adaptive sparse partial least squares algorithm

A Mini-review of Computational Approaches to Predict Functions and Findings of Novel Micro Peptides

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New techniques in bioinformatics and the study of the transcriptome at a wide-scale have uncovered the fact that a large part of the genome is being translated than recently perceived thoughts and research, bringing about the creation of a various quantity of RNA with proteincoding and noncoding potential. A lot of RNA particles have been considered as noncoding due to many reasons, according to developing proofs. Like many sORFs that encode many functional micro peptides have neglected due to their tiny sizes.

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Advanced studies reveal many major biological functions of these sORFs and their encoded micro peptides in a different and wide range of species. All the achievement in the identification of these sORFs and micro peptides is due to the progressive bioinformatics and high-throughput sequencing methods. This field has pulled in more consideration due to the detection of a large number of more sORFs and micro peptides. Nowadays, COVID-19 grabs all the attention of science as it is a sudden outbreak. sORFs of COVID-19 should be revealed for new ways to understand this virus. This review discusses ongoing progress in the systems for the identification and distinguishing proof of sORFs and micro peptides.

Alternative ORFs and small ORFs: shedding light on the dark proteome

Traditional annotation of protein-encoding genes relied on assumptions, such as one open reading frame (ORF) encodes one protein and minimal lengths for translated proteins. With the serendipitous discoveries of translated ORFs encoded upstream and downstream of annotated ORFs, from alternative start sites nested within annotated ORFs and from RNAs previously considered noncoding, it is becoming clear that these initial assumptions are incorrect. The findings have led to the realization that genetic information is more densely coded and that the proteome is more complex than previously anticipated. As such, interest in the identification and characterization of the previously ignored 'dark proteome' is increasing, though we note that research in eukaryotes and bacteria has largely progressed in isolation. To bridge this gap and illustrate exciting findings emerging from studies of the dark proteome, we highlight recent advances in both eukaryotic and bacterial cells. We discuss progress in the detection of alternative ORFs as well as in the understanding of functions and the regulation of their expression and posit questions for future work.

ESDA: An Improved Approach to Accurately Identify Human snoRNAs for Precision Cancer Therapy

Background:

SnoRNAs (Small nucleolar RNAs) are small RNA molecules with approximately 60-300 nucleotides in sequence length. They have been proved to play important roles in cancer occurrence and progression. It is of great clinical importance to identify new snoRNAs as fast and accurately as possible.

Objective:

A novel algorithm, ESDA (Elastically Sparse Partial Least Squares Discriminant Analysis), was proposed to improve the speed and the performance of recognizing snoRNAs from other RNAs in human genomes.

Methods:

In ESDA algorithm, to optimize the extracted information, kernel features were selected from the variables extracted from both primary sequences and secondary structures. Then they were used by SPLSDA (sparse partial least squares discriminant analysis) algorithm as input variables for the final classification model training to distinguish snoRNA sequences from other Human RNAs. Due to the fact that no prior biological knowledge is request to optimize the classification model, ESDA is a very practical method especially for completely new sequences.

Results:

89 H/ACA snoRNAs and 269 C/D snoRNAs of human were used as positive samples and 3403 non-snoRNAs as negative samples to test the identification performance of the proposed ESDA. For the H/ACA snoRNAs identification, the sensitivity and specificity were respectively as high as 99.6% and 98.8%. For C/D snoRNAs, they were respectively 96.1% and 98.3%. Furthermore, we compared ESDA with other widely used algorithms and classifiers: SnoReport, RF (Random Forest), DWD (Distance Weighted Discrimination) and SVM (Support Vector Machine). The highest improvement of accuracy obtained by ESDA was 25.1%.

Conclusion:

Strongly proved the superiority performance of ESDA and make it promising for identifying SnoRNAs for further development of the precision medicine for cancers.

Gene Prediction in Metagenomic Fragments with Deep Learning

Next generation sequencing technologies used in metagenomics yield numerous sequencing fragments which come from thousands of different species. Accurately identifying genes from metagenomics fragments is one of the most fundamental issues in metagenomics. In this article, by fusing multifeatures (i.e., monocodon usage, monoamino acid usage, ORF length coverage, and Z-curve features) and using deep stacking networks learning model, we present a novel method (called Meta-MFDL) to predict the metagenomic genes. The results with 10 CV and independent tests show that Meta-MFDL is a powerful tool for identifying genes from metagenomic fragments.

Comparative genomic analysis shows that avian pathogenic Escherichia coli isolate IMT5155 (O2:K1:H5; ST complex 95, ST140) shares close relationship with ST95 APEC O1:K1 and human ExPEC O18:K1 strains

Avian pathogenic E. coli and human extraintestinal pathogenic E. coli serotypes O1, O2 and O18 strains isolated from different hosts are generally located in phylogroup B2 and ST complex 95, and they share similar genetic characteristics and pathogenicity, with no or minimal host specificity. They are popular objects for the study of ExPEC genetic characteristics and pathogenesis in recent years. Here, we investigated the evolution and genetic blueprint of APEC pathotype by performing phylogenetic and comparative genome analysis of avian pathogenic E. coli strain IMT5155 (O2:K1:H5; ST complex 95, ST140) with other E. coli pathotypes. Phylogeny analyses indicated that IMT5155 has closest evolutionary relationship with APEC O1, IHE3034, and UTI89. Comparative genomic analysis showed that IMT5155 and APEC O1 shared significant genetic overlap/similarities with human ExPEC dominant O18:K1 strains (IHE3034 and UTI89). Furthermore, the unique PAI I₅₁₅₅ (GI-12) was identified and found to be conserved in APEC O2 serotype isolates. GI-7 and GI-16 encoding two typical T6SSs in IMT5155 might be useful markers for the identification of ExPEC dominant serotypes (O1, O2, and O18) strains. IMT5155 contained a ColV plasmid p1ColV₅₁₅₅, which defined the APEC pathotype. The distribution analysis of 10 sequenced ExPEC pan-genome virulence factors among 47 sequenced E. coli strains provided meaningful information for B2 APEC/ExPEC-specific virulence factors, including several adhesins, invasins, toxins, iron acquisition systems, and so on. The pathogenicity tests of IMT5155 and other APEC O1:K1 and O2:K1 serotypes strains (isolated in China) through four animal models showed that they were highly virulent for avian colisepticemia and able to cause septicemia and meningitis in neonatal rats, suggesting zoonotic potential of these APEC O1:K1 and O2:K1 isolates.