Computational prediction of Caenorhabditis box H/ACA snoRNAs using genomic properties of their host genes

Sequencing of individual barcoded cDNAs using Pacific Biosciences and Oxford Nanopore technologies reveals platform-specific error patterns

Long-read transcriptomics require understanding error sources inherent to technologies. Current approaches cannot compare methods for an individual RNA molecule. Here, we present a novel platform-comparison method that combines barcoding strategies and long-read sequencing to sequence cDNA copies representing an individual RNA molecule on both Pacific Biosciences (PacBio) and Oxford Nanopore Technologies (ONT). We compare these long-read pairs in terms of sequence content and isoform patterns. Although individual read pairs show high similarity, we find differences in (1) aligned length, (2) transcription start site (TSS), (3) polyadenylation site (poly(A)-site) assignment, and (4) exon–intron structures. Overall, 25% of read pairs disagree on either TSS, poly(A)-site, or splice site. Intron-chain disagreement typically arises from alignment errors of microexons and complicated splice sites. Our single-molecule technology comparison reveals that inconsistencies are often caused by sequencing error–induced inaccurate ONT alignments, especially to downstream GUNNGU donor motifs. However, annotation-disagreeing upstream shifts in NAGNAG acceptors in ONT are often confirmed by PacBio and are thus likely real. In both barcoded and nonbarcoded ONT reads, we find that intron number and proximity of GU/AGs better predict inconsistencies with the annotation than read quality alone. We summarize these findings in an annotation-based algorithm for spliced alignment correction that improves subsequent transcript construction with ONT reads.

H/ACA Small Ribonucleoproteins: Structural and Functional Comparison Between Archaea and Eukaryotes

During ribosome synthesis, ribosomal RNA is modified through the formation of many pseudouridines and methylations which contribute to ribosome function across all domains of life. In archaea and eukaryotes, pseudouridylation of rRNA is catalyzed by H/ACA small ribonucleoproteins (sRNPs) utilizing different H/ACA guide RNAs to identify target uridines for modification. H/ACA sRNPs are conserved in archaea and eukaryotes, as they share a common general architecture and function, but there are also several notable differences between archaeal and eukaryotic H/ACA sRNPs. Due to the higher protein stability in archaea, we have more information on the structure of archaeal H/ACA sRNPs compared to eukaryotic counterparts. However, based on the long history of yeast genetic and other cellular studies, the biological role of H/ACA sRNPs during ribosome biogenesis is better understood in eukaryotes than archaea. Therefore, this review provides an overview of the current knowledge on H/ACA sRNPs from archaea, in particular their structure and function, and relates it to our understanding of the roles of eukaryotic H/ACA sRNP during eukaryotic ribosome synthesis and beyond. Based on this comparison of our current insights into archaeal and eukaryotic H/ACA sRNPs, we discuss what role archaeal H/ACA sRNPs may play in the formation of ribosomes.

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.

Features for computational operon prediction in prokaryotes

Accurate prediction of operons can improve the functional annotation and application of genes within operons in prokaryotes. Here, we review several features: (i) intergenic distance, (ii) metabolic pathways, (iii) homologous genes, (iv) promoters and terminators, (v) gene order conservation, (vi) microarray, (vii) clusters of orthologous groups, (viii) gene length ratio, (ix) phylogenetic profiles, (x) operon length/size and (xi) STRING database scores, as well as some other features, which have been applied in recent operon prediction methods in prokaryotes in the literature. Based on a comparison of the prediction performances of these features, we conclude that other, as yet undiscovered features, or feature selection with a receiver operating characteristic analysis before algorithm processing can improve operon prediction in prokaryotes.

The nucleolus of Caenorhabditis elegans

Nucleolar size and appearance correlate with ribosome biogenesis and cellular activity. The mechanisms underlying changes in nucleolar appearance and regulation of nucleolar size that occur during differentiation and cell cycle progression are not well understood. Caenorhabditis elegans provides a good model for studying these processes because of its small size and transparent body, well-characterized cell types and lineages, and because its cells display various sizes of nucleoli. This paper details the advantages of using C. elegans to investigate features of the nucleolus during the organism's development by following dynamic changes in fibrillarin (FIB-1) in the cells of early embryos and aged worms. This paper also illustrates the involvement of the ncl-1 gene and other possible candidate genes in nucleolar-size control. Lastly, we summarize the ribosomal proteins involved in life span and innate immunity, and those homologous genes that correspond to human disorders of ribosomopathy.

Family size and turnover rates among several classes of small non-protein-coding RNA genes in Caenorhabditis nematodes

It is important to understand the forces that shape the size and evolutionary histories of gene families. Here, we investigated the evolution of non–protein-coding RNA genes in the genomes of Caenorhabditis nematodes. We specifically focused on nested arrangements, that is, cases in which an RNA gene is entirely contained in an intron of another gene. Comparing these arrangements between species simplifies the inference of orthology and, therefore, of evolutionary fates of nested genes. Two distinct patterns are evident in the data. Genes encoding small nuclear RNAs (snRNAs) and transfer RNAs form large families, which have persisted since before the common ancestor of Metazoa. Yet, individual genes die relatively rapidly, with few orthologs having survived since the divergence of Caenorhabditis elegans and Caenorhabditis briggsae. In contrast, genes encoding small nucleolar RNAs (snoRNAs) are either single-copy or form small families. Individual snoRNAs turn over at a relatively slow rate—most C. elegans genes have clearly identifiable orthologs in C. briggsae. We also found that in Drosophila, genes from larger snRNA families die at a faster rate than their counterparts from single-gene families. These results suggest that a relationship between family size and the rate of gene turnover may be a general feature of genome evolution.