ShortStack: Comprehensive annotation and quantification of small RNA genes

Small RNAs hit the big time

An interesting feature of trypanosome genome organization involves genes transcribed by RNA polymerase III. The U6 small nuclear RNA (snRNA), U-snRNA B (the U3 snRNA homolog), and 7SL RNA genes are closely linked with different, divergently oriented tRNA genes. To test the hypothesis that this association is of functional significance, we generated deletion and block substitution mutants of all three small RNA genes and monitored their effects by transient expression in cultured insect-form cells of Trypanosoma brucei. In each case, two extragenic regulatory elements were mapped to the A and B boxes of the respective companion tRNA gene. In addition, the tRNA(Thr) gene, which is upstream of the U6 snRNA gene, was shown by two different tests to be expressed in T. brucei cells, thus confirming its identity as a gene. This association between tRNA and small RNA genes appears to be a general phenomenon in the family Trypanosomatidae, since it is also observed at the U6 snRNA loci in Leishmania pifanoi and Crithidia fasciculata and at the 7SL RNA locus in L. pifanoi. We propose that the A- and B-box elements of small RNA-associated tRNA genes serve a dual role as intragenic promoter elements for the respective tRNA genes and as extragenic regulatory elements for the linked small RNA genes. The possible role of tRNA genes in regulating small RNA gene transcription is discussed.

An interesting feature of trypanosome genome organization involves genes transcribed by RNA polymerase III. The U6 small nuclear RNA (snRNA), U-snRNA B (the U3 snRNA homolog), and 7SL RNA genes are closely linked with different, divergently oriented tRNA genes. To test the hypothesis that this association is of functional significance, we generated deletion and block substitution mutants of all three small RNA genes and monitored their effects by transient expression in cultured insect-form cells of Trypanosoma brucei. In each case, two extragenic regulatory elements were mapped to the A and B boxes of the respective companion tRNA gene. In addition, the tRNA(Thr) gene, which is upstream of the U6 snRNA gene, was shown by two different tests to be expressed in T. brucei cells, thus confirming its identity as a gene. This association between tRNA and small RNA genes appears to be a general phenomenon in the family Trypanosomatidae, since it is also observed at the U6 snRNA loci in Leishmania pifanoi and Crithidia fasciculata and at the 7SL RNA locus in L. pifanoi. We propose that the A- and B-box elements of small RNA-associated tRNA genes serve a dual role as intragenic promoter elements for the respective tRNA genes and as extragenic regulatory elements for the linked small RNA genes. The possible role of tRNA genes in regulating small RNA gene transcription is discussed.

Genomic analysis of the human transcriptome has been made possible only in last decade by next generation sequencing (NGS). Recent advancements in NGS has further allowed us to look into small non-coding RNAs (sncRNAs) such as microRNAs (miRNAs), Piwi-interacting-RNAs (piRNAs), long non-coding RNAs (lncRNAs) & small nuclear/nucleolar RNAs (sn/snoRNAs). Recently, the roles of sncRNAs in biological processes have been implicated in biomarker development for diagnosis, prognosis &therapy. In the present study, 50 colon cancer patient’s small RNA sequencing raw data was downloaded from NIH bioproject (PRJNA266667; 7 TNM stage II & 43 TNM stage III) which contained 27 female & 23 male samples. 24 samples had metachronous metastasis (MM) & 26 non-metachronous metastasis (NMM). The small RNA-seq data was analyzed using PartekFlow. In depth analysis showed aberrant expression of 48 miRNAs (all upregulated) in TNM-III vs TNM-II specimens & 20 miRNAs (17 upregulated & 3 downregulated) in MM vs NMM. Further investigation of dysregulated miRNA through pathway analysis confirmed that the majority of the miRNAs were involved in cancer signaling pathways. Analysis of piRNA found unusual expression of 60 piRNAs (57 upregulated & 3 downregulated) in TNM-III vs TNM-II & 31 piRNAs (28 upregulated & 3 downregulated) in MM vs NMM. Further analysis of long non-coding RNAs, we found 77 lncRNAs were significantly expressed in TNM-III vs TNM-II &18 lncRNAs in MM vs NMM. We also, investigated small nuclear/nucleolar RNAs (sn/snoRNAs), miscRNAs & mtRNAs, we identified 37 snRNAs, 105 snoRNAs, 28 miscRNAs & 5 mtRNAs in TNM-III vs TNM-II whereas 2 snRNAs, 11 snoRNAs, 57 miscRNAs & 8 mtRNAs in MM vs NMM were identified. In summary, our comprehensive analysis on publicly available small RNA-seq data identified multiple small non-coding RNAs that need to be further explored for their use in the prognosis, diagnosis & therapy of colon cancer. Citation Format: Srinivas V. Koduru, Angelique Nyinawabera, Dino J. Ravnic, Amit K. Tiwari. Interrogation of small RNA-seq data for small noncoding RNA in human colon cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 3490. doi:10.1158/1538-7445.AM2017-3490

Non-coding RNAs (ncRNAs) play vital roles in translation, splicing, RNA processing, RNA modification and regulation of gene expression. The advancement in ncRNA discovery is evolving along with the finding of new classes of ncRNAs and the invention of revolutionary sequencing platforms. High-throughput sequencing technologies greatly facilitate the study of small regulatory RNAs which are 20 to 30 nt in length. High-throughput sequencing data of 18-26 nt small RNA fragments are a mixture of small regulatory RNAs and degraded products from coding RNAs or ncRNAs. The proper choice of computational approaches in analyzing small RNA sequencing data is crucial for the dissection of small RNAs derived from distinct origins, for making discovery of new ncRNAs and for revealing embedded knowledge in these ncRNAs. To date, the development of computational approaches mostly focused on the discovery of microRNAs (miRNAs). Computational approaches which use small RNA sequencing data for the studies of other ncRNAs are much in need. This dissertation presents the development of novel bioinformatics approaches to analyze small RNA sequencing data and showed that the analyses have increased the understandings of Arabidopsis ncRNAs. In first part, by the use of abundant small RNA sequencing data from the public domain, a new bioinformatics approach was developed for the finding of trans-acting small interfering RNAs (ta-siRNAs), a new class of small regulatory RNAs. Different from that of other siRNAs, the biogenesis of ta-siRNAs is dependent on the cleavage directed by miRNAs. Moreover, most ta-siRNAs are clustered in 21-nt increments relative to the cleavage site. Based on this characteristic, this study developed the first computational algorithm which successfully recovered both known and novel Arabidopsis loci producing ta-siRNAs from complex small RNA sequencing data. A group of newly identified ta-siRNAs was produced by the cleavage directed by a ta-siRNA instead of by miRNAs as was reported previously. The results indicate the existence of a small RNA regulatory cascade initiated by miRNA-directed cleavage and followed by the consecutive production of ta-siRNAs. The second part focuses on the use of small RNA sequencing data in the annotation of small nucleolar RNAs (snoRNAs). Small RNAs from snoRNAs are often considered to be degraded products of snoRNAs and were filtered out without further analysis in previous studies. However, the analysis of Arabidopsis small RNA sequencing data revealed an enrichment of small RNAs at the termini of snoRNAs. With the use of this feature, this study developed a new method which was able to re-annotate known snoRNAs lacking well defined termini and to discover novel snoRNA species. The finding of new snoRNAs also supported that there are additional RNA modification sites on Arabidopsis ribosomal RNAs and spliceosomal small nuclear RNAs. This research demonstrates that, by combining pre-existing biological knowledge and appropriate mining approaches, small RNA sequencing data represent a wealth treasure for the studies of small regulatory RNAs as well as other ncRNAs.

*Norway spruce expresses a temperature-dependent epigenetic memory from the time of embryo development, which thereafter influences the timing bud phenology. MicroRNAs (miRNAs)are endogenous small RNAs, exerting epigenetic gene regulatory impacts. We have tested for their presence and differential expression. *We prepared concatemerized small RNA libraries from seedlings of two full-sib families, originated from seeds developed in a cold and warm environment. One family expressed distinct epigenetic effects while the other not. We used available plant miRNA query sequences to search for conserved miRNAs and from the sequencing we found novel ones; the miRNAs were monitored using relative real time-PCR. *Sequencing identified 24 novel and four conserved miRNAs. Further screening of the conserved miRNAs confirmed the presence of 16 additional miRNAs. Most of the miRNAs were targeted to unknown genes. The expression of seven conserved and nine novel miRNAs showed significant differences in transcript levels in the full-sib family showing distinct epigenetic difference in bud set, but not in the nonresponding full-sib family. Putative miRNA targets were studied. *Norway spruce contains a set of conserved miRNAs as well as a large proportion of novel nonconserved miRNAs. The differentially expression of specific miRNAs indicate their putative participation in the epigenetic regulation.

Background: Improved healthcare and recent breakthroughs in technology have substantially reduced cancer mortality rates worldwide. Recent advancements in next-generation sequencing (NGS) have allowed genomic analysis of the human transcriptome. Now, using NGS we can further look into small non-coding regions of RNAs (sncRNAs) such as microRNAs (miRNAs), Piwi-interacting-RNAs (piRNAs), long non-coding RNAs (lncRNAs), and small nuclear/nucleolar RNAs (sn/snoRNAs) among others. Recent studies looking at sncRNAs indicate their role in important biological processes such as cancer progression and predict their role as biomarkers for disease diagnosis, prognosis, and therapy. Results: In the present study, we data mined publically available small RNA sequencing data from colorectal tissue samples of eight matched patients (benign, tumor, and metastasis) and remapped the data for various small RNA annotations. We identified aberrant expression of 13 miRNAs in tumor and metastasis specimens [tumor vs benign group (19 miRNAs) and metastasis vs benign group (38 miRNAs)] of which five were upregulated, and eight were downregulated, during disease progression. Pathway analysis of aberrantly expressed miRNAs showed that the majority of miRNAs involved in colon cancer were also involved in other cancers. Analysis of piRNAs revealed six to be over-expressed in the tumor vs benign cohort and 24 in the metastasis vs benign group. Only two piRNAs were shared between the two cohorts. Examining other types of small RNAs [sn/snoRNAs, mt_rRNA, miscRNA, nonsense mediated decay (NMD), and rRNAs] identified 15 sncRNAs in the tumor vs benign group and 104 in the metastasis vs benign group, with only four others being commonly expressed. Conclusion: In summary, our comprehensive analysis on publicly available small RNA-seq data identified multiple differentially expressed sncRNAs during colorectal cancer progression at different stages compared to normal colon tissue. We speculate that deciphering and validating the roles of sncRNAs may prove useful in colorectal cancer prognosis, diagnosis, and therapy.

Large blocks of tandemly repeated DNAs—satellite DNAs (satDNAs)—play important roles in heterochromatin formation and chromosome segregation. We know little about how satDNAs are regulated; however, their misregulation is associated with genomic instability and human diseases. We use the Drosophila melanogaster germline as a model to study the regulation of satDNA transcription and chromatin. Here we show that complex satDNAs (>100-bp repeat units) are transcribed into long noncoding RNAs and processed into piRNAs (PIWI interacting RNAs). This satDNA piRNA production depends on the Rhino-Deadlock-Cutoff complex and the transcription factor Moonshiner—a previously described non-canonical pathway that licenses heterochromatin-dependent transcription of dual-strand piRNA clusters. We show that this pathway is important for establishing heterochromatin at satDNAs. Therefore, satDNAs are regulated by piRNAs originating from their own genomic loci. This novel mechanism of satDNA regulation provides insight into the role of piRNA pathways in heterochromatin formation and genome stability.

Virus-encoded small RNAs (vsRNA) have been reported to play an important role in viral infection. Unfortunately, there is still a lack of an effective method for vsRNA identification. Herein, we presented vsRNAfinder, a de novo method for identifying high-confidence vsRNAs from small RNA-Seq (sRNA-Seq) data based on peak calling and Poisson distribution and is publicly available at https://github.com/ZenaCai/vsRNAfinder. vsRNAfinder outperformed two widely used methods namely miRDeep2 and ShortStack in identifying viral miRNAs with a significantly improved sensitivity. It can also be used to identify sRNAs in animals and plants with similar performance to miRDeep2 and ShortStack. vsRNAfinder would greatly facilitate effective identification of vsRNAs from sRNA-Seq data.

Many small RNAs contribute essential activities to eukaryotic cells. In mammalian genomes dispersed repetitive sequences which exhibit homology to small RNAs often exist as pseudogenes which can complicate identification, localization, and analysis of the authentic gene. We mapped a productive human 7SK small nuclear RNA gene to human chromosome 6 by analyzing Northern blots derived from a panel of somatic cell hybrids that contain single human chromosomes. In order to avoid crossreactivity of the probe with rodent 7SK RNA, which is 98% identical to human 7SK, a method termed homolog exclusion probing was developed. This method uses an excess of non-labelled rodent-specific oligodeoxynucleotide to block the rodent 7SK RNA from hybridizing with the human-specific oligodeoxynucleotide probe. The effectiveness of this method to enhance the human 7SK RNA signal is demonstrated. The potential to map and subsequently isolate other small RNA genes by this approach and the use of homolog exclusion probing to discriminate among family members of highly related RNAs and DNAs in a single species is discussed.

MicroRNAs (miRNAs) are small (20-24 nucleotides) non-coding ribo-regulatory molecules with significant roles in regulating target mRNA and long non-coding RNAs at transcriptional and post-transcriptional levels. Rapid advancement in the small RNA sequencing methods with integration of degradome sequencing has accelerated the understanding of miRNA-mediated regulatory hubs in plants and yielded extensive annotation of miRNAs and corresponding targets. However, it is becoming clear that large numbers of such annotations are questionable. Therefore, it is imperative to adopt reliable and strict bioinformatics pipelines for miRNA identification. Furthermore, sensitive methods are needed for validation and functional characterization of miRNA and its target(s). In this chapter, we have provided a comprehensive and streamlined methodology for miRNA identification and its functional validation in plants. This includes a combination of various in silico and experimental methodologies. To identify miRNA compendium from large-scale Next-Generation Sequencing (NGS) small RNA datasets, the miR-PREFeR (miRNA PREdiction From small RNA-Seq data) bioinformatics tool has been described. Also, a homology-based search protocol for finding members of a specific miRNA family has been discussed. The chapter also includes techniques to ascertain miRNA:target pair specificity using in silico target prediction from degradome NGS libraries using CleaveLand pipeline, miRNA:target validation by in planta transient assays, 5' RLM-RACE and expression analysis as well as functional techniques like miRNA overexpression, short tandem target mimic and resistant target approaches. The proposed strategy offers a reliable and sensitive way for miRNA:target identification and validation. Additionally, we strongly promulgate the use of multiple methodologies to validate a miRNA as well as its target.

MicroRNAs (miRNAs) are small noncoding RNAs (sncRNAs) that function in post-transcriptional gene regulation through imperfect base pairing with mRNA targets, which results in inhibition of translation and typically destabilization of bound transcripts. Sequence-based algorithms historically used to predict miRNA targets face inherent challenges in reliably reflecting in vivo interactions. Recent strategies have directly profiled miRNA–target interactions by crosslinking and ligation of sncRNAs to their targets within the RNA-induced silencing complex (RISC), followed by high-throughput sequencing of the chimeric sncRNA:target RNAs. Despite the strength of these direct profiling approaches, standardized pipelines for effectively analyzing the resulting chimeric sncRNA:target RNA sequencing data are not readily available. Here we present SCRAP, a robust small chimeric RNA analysis pipeline for the bioinformatic processing of chimeric sncRNA:target RNA sequencing data. SCRAP consists of two parts, each of which is specifically optimized for the distinctive characteristics of chimeric small RNA sequencing reads: first, read processing and alignment and second, peak calling and annotation. We apply SCRAP to benchmark chimeric sncRNA:target RNA sequencing data sets generated by distinct molecular approaches, and compare SCRAP to existing chimeric RNA analysis pipelines. SCRAP has minimal hardware requirements, is cross-platform, and contains extensive annotations to broaden accessibility for processing small chimeric RNA sequencing data and enable insights into the targets of small noncoding RNAs in regulating diverse biological systems.

Protocol for identifying Dicer as dsRNA binding and cleaving reagent in response to transfected dsRNA

MicroRNAs (miRNAs) are small non-protein-coding RNAs that regulate gene expression in many eukaryotes. Next-generation sequencing of small RNAs (small RNA-seq) is central to the discovery and annotation of miRNAs. Newly annotated miRNAs and their longer precursors encoded by MIRNA loci are typically submitted to databases such as the miRBase microRNA registry following the publication of a peer-reviewed study. However, genome-wide scans using small RNA-seq data often yield high rates of false-positive MIRNA annotations, highlighting the need for more robust validation methods. miRScore was developed as an independent and efficient tool for evaluating new MIRNA annotations using sRNA-seq data. miRScore combines structural and expression-based analyses to provide rapid and reliable validation of new MIRNA annotations. By providing users with detailed metrics and visualization, miRScore enhances the ability to assess confidence in MIRNA annotations. miRScore has the potential to advance the overall quality of MIRNA annotations by improving accuracy of new submissions to miRNA databases and serving as a resource for re-evaluating existing annotations.

MicroRNAs (miRNAs) are small non-protein-coding RNAs that regulate gene expression in many eukaryotes. Next-generation sequencing of small RNAs (small RNA-seq) is central to the discovery and annotation of miRNAs. Newly annotated miRNAs and their longer precursors encoded by MIRNA loci are typically submitted to databases such as the miRBase microRNA registry following the publication of a peer-reviewed study. However, genome-wide scans using small RNA-seq data often yield high rates of false-positive MIRNA annotations, highlighting the need for more robust validation methods. miRScore was developed as an independent and efficient tool for evaluating new MIRNA annotations using sRNA-seq data. miRScore combines structural and expression-based analyses to provide rapid and reliable validation of new MIRNA annotations. By providing users with detailed metrics and visualization, miRScore enhances the ability to assess confidence in MIRNA annotations. miRScore has the potential to advance the overall quality of MIRNA annotations by improving accuracy of new submissions to miRNA databases and serving as a resource for re-evaluating existing annotations.