Michał Wojciech Szcześniak

A survey of best practices for RNA-seq data analysis

RNA-sequencing (RNA-seq) has a wide variety of applications, but no single analysis pipeline can be used in all cases. We review all of the major steps in RNA-seq data analysis, including experimental design, quality control, read alignment, quantification of gene and transcript levels, visualization, differential gene expression, alternative splicing, functional analysis, gene fusion detection and eQTL mapping. We highlight the challenges associated with each step. We discuss the analysis of small RNAs and the integration of RNA-seq with other functional genomics techniques. Finally, we discuss the outlook for novel technologies that are changing the state of the art in transcriptomics.

CANTATAdb: A Collection of Plant Long Non-Coding RNAs

Long non-coding RNAs (lncRNAs) represent a class of potent regulators of gene expression that are found in a wide array of eukaryotes; however, our knowledge about these molecules in plants is still very limited. In particular, a number of model plant species still lack comprehensive data sets of lncRNAs and their annotations, and very little is known about their biological roles. To meet these shortcomings, we created an online database of lncRNAs in 10 model plant species. The lncRNAs were identified computationally using dozens of publicly available RNA sequencing (RNA-Seq) libraries. Expression values, coding potential, sequence alignments as well as other types of data provide annotation for the identified lncRNAs. In order to better characterize them, we investigated their potential roles in splicing modulation and deregulation of microRNA functions. The data are freely available for searching, browsing and downloading from an online database called CANTATAdb (http://cantata.amu.edu.pl, http://yeti.amu.edu.pl/CANTATA/).

Primate and Rodent Specific Intron Gains and the Origin of Retrogenes with Splice Variants

Retroposition, a leading mechanism for gene duplication, is an important process shaping the evolution of genomes. Retrogenes are also involved in the gene structure evolution as a major player in the process of intron deletion. Here, we demonstrate the role of retrogenes in intron gain in mammals. We identified one case of “intronization,” the transformation of exonic sequences into an intron, in the primate specific retrogene RNF113B and two independent “intronization” events in the retrogene DCAF12L2, one in the common ancestor of primates and rodents and another one in the rodent lineage. Intron gain resulted from the origin of new splice variants, and both genes have two transcript forms, one with retained intron and one with the intron spliced out. Evolution of these genes, especially RNF113B, has been very dynamic and has been accompanied by several additional events including parental gene loss, secondary retroposition, and exaptation of transposable elements.

HuntMi: an efficient and taxon-specific approach in pre-miRNA identification

BackgroundMachine learning techniques are known to be a powerful way of distinguishing microRNA hairpins from pseudo hairpins and have been applied in a number of recognised miRNA search tools. However, many current methods based on machine learning suffer from some drawbacks, including not addressing the class imbalance problem properly. It may lead to overlearning the majority class and/or incorrect assessment of classification performance. Moreover, those tools are effective for a narrow range of species, usually the model ones. This study aims at improving performance of miRNA classification procedure, extending its usability and reducing computational time.ResultsWe present HuntMi, a stand-alone machine learning miRNA classification tool. We developed a novel method of dealing with the class imbalance problem called ROC-select, which is based on thresholding score function produced by traditional classifiers. We also introduced new features to the data representation. Several classification algorithms in combination with ROC-select were tested and random forest was selected for the best balance between sensitivity and specificity. Reliable assessment of classification performance is guaranteed by using large, strongly imbalanced, and taxon-specific datasets in 10-fold cross-validation procedure. As a result, HuntMi achieves a considerably better performance than any other miRNA classification tool and can be applied in miRNA search experiments in a wide range of species.ConclusionsOur results indicate that HuntMi represents an effective and flexible tool for identification of new microRNAs in animals, plants and viruses. ROC-select strategy proves to be superior to other methods of dealing with class imbalance problem and can possibly be used in other machine learning classification tasks. The HuntMi software as well as datasets used in the research are freely available at http://lemur.amu.edu.pl/share/HuntMi/.

lncRNA-RNA Interactions across the Human Transcriptome.

Long non-coding RNAs (lncRNAs) represent a numerous class of non-protein coding transcripts longer than 200 nucleotides. There is possibility that a fraction of lncRNAs are not functional and represent mere transcriptional noise but a growing body of evidence shows they are engaged in a plethora of molecular functions and contribute considerably to the observed diversification of eukaryotic transcriptomes and proteomes. Still, however, only ca. 1% of lncRNAs have well established functions and much remains to be done towards decipherment of their biological roles. One of the least studied aspects of lncRNAs biology is their engagement in gene expression regulation through RNA-RNA interactions. By hybridizing with mate RNA molecules, lncRNAs could potentially participate in modulation of pre-mRNA splicing, RNA editing, mRNA stability control, translation activation, or abrogation of miRNA-induced repression. Here, we implemented a similarity-search based method for transcriptome-wide identification of RNA-RNA interactions, which enabled us to find 18,871,097 lncRNA-RNA base-pairings in human. Further analyses showed that the interactions could be involved in processing, stability control and functions of 57,303 transcripts. An extensive use of RNA-Seq data provided support for approximately one third of the interactions, at least in terms of the two RNA components being co-expressed. The results suggest that lncRNA-RNA interactions are broadly used to regulate and diversify the human transcriptome.

miRNEST 2.0: a database of plant and animal microRNAs

Ever growing interest in microRNAs has immensely populated the number of resources and research papers devoted to the field and, as a result, it becomes more and more demanding to find miRNA data of interest. To mitigate this problem, we created miRNEST database (http://mirnest.amu.edu.pl), an integrative microRNAs resource. In its updated version, named miRNEST 2.0, the database is complemented with our extensive miRNA predictions from deep sequencing libraries, data from plant degradome analyses, results of pre-miRNA classification with HuntMi and miRNA splice sites information. We also added download and upload options and improved the user interface to make it easier to browse through miRNA records.

ERISdb: A Database of Plant Splice Sites and Splicing Signals

Splicing is one of the major contributors to observed spatiotemporal diversification of transcripts and proteins in metazoans. There are numerous factors that affect the process, but splice sites themselves along with the adjacent splicing signals are critical here. Unfortunately, there is still little known about splicing in plants and, consequently, further research in some fields of plant molecular biology will encounter difficulties. Keeping this in mind, we performed a large-scale analysis of splice sites in eight plant species, using novel algorithms and tools developed by us. The analyses included identification of orthologous splice sites, polypyrimidine tracts and branch sites. Additionally we identified putative intronic and exonic cis-regulatory motifs, U12 introns as well as splice sites in 45 microRNA genes in five plant species. We also provide experimental evidence for plant splice sites in the form of expressed sequence tag and RNA-Seq data. All the data are stored in a novel database called ERISdb and are freely available at http://lemur.amu.edu.pl/share/ERISdb/.

Identification of apple miRNAs and their potential role in fire blight resistance

MicroRNAs (miRNAs) are key players in multiple biological processes; therefore, analysis and characterization of these small regulatory RNAs is a critical step toward a better understanding of animal and plant biology. In apple (Malus domestica), 200 microRNAs are known, which most probably represent only a fraction of miRNAome diversity. As a result, more effort is required to better annotate miRNAs and their functions in this economically important species. We performed deep sequencing of 12 small RNA libraries obtained for fire blight-resistant and fire blight-sensitive trees. In the sequencing results, we identified 116 novel microRNAs and confirmed a majority of previously reported apple miRNAs. We then experimentally verified selected candidates with RT-PCR and stem-loop quantitative PCR (qPCR) and performed differential expression analysis. Finally, we identified and characterized putative targets of all known apple miRNAs. The gene ontology (GO) enrichment analysis suggests prominent roles of miRNAs in response to stresses, including pathogen infection. In this study, we identified 116 new and confirmed the expression of 143 already known miRNAs. Moreover, our data suggests that apple microRNAs might be considered as regulators and markers of fire blight resistance. The analyses we performed allowed us to define four apple miRNAs potentially involved in fire blight resistance in apple trees: mdm-miR169a, mdm-miR160e, mdm-miR167b-g, and mdm-miR168a,b. These miRNAs are known to be involved in response to stresses across other plant species, usually by targeting stress response proteins. The relatively low number of candidates may result from the high variance of biological replicates and the fact that stress response miRNAs are usually induced by the stress factors and frequently expressed at a low level, or not expressed at all, in normal conditions. The results of our studies are freely available in an online database at http://lemur.amu.edu.pl/share/apple_miRNAs/.

Erratum to: A survey of best practices for RNA-seq data analysis

Erratum During editing of the article by Conesa et al. [1], an error was introduced to some of the citations, such that incorrect references were provided for some articles the second time they were cited. The following sentences are affected: Algorithms that quantify expression from transcriptome mappings include RSEM (RNA-Seq by Expectation Maximization) [40], eXpress [41], Sailfish [35] and kallisto [42] among others. These methods allocate multi-mapping reads among transcript and output within-sample normalized values corrected for sequencing biases [35, 41, 43]. The citation for Sailfish should be [34] (Patro et al., Nat Biotechnol. 2014;32:463–4) in both sentences. Additional factors that interfere with intra-sample comparisons include changes in transcript length across samples or conditions [50], positional biases in coverage along the transcript (which are accounted for in Cufflinks), average fragment size [43], and the GC contents of genes (corrected in the EDAseq package [21]). The citation for EDAseq should be [20] (Risso et al. BMC Bioinformatics. 2011;12:480) The NOISeq R package [20] contains a wide variety of diagnostic plots to identify sources of biases in RNA-seq data and to apply appropriate normalization procedures in each case. The citation for NOISeq should be [19] (Tarazona et al. Nucleic Acids Res. 2015;43:e140) These effects can be minimized by appropriate experimental design [51] or, alternatively, removed by batch-correction methods such as COMBAT [52] or ARSyN [20, 53].

Collagen synthesis disruption and downregulation of core elements of TGF-β, Hippo, and Wnt pathways in keratoconus corneas.

To understand better the factors contributing to keratoconus (KTCN), we performed comprehensive transcriptome profiling of human KTCN corneas for the first time using an RNA-Seq approach. Twenty-five KTCN and 25 non-KTCN corneas were enrolled in this study. After RNA extraction, total RNA libraries were prepared and sequenced. The discovery RNA-Seq analysis (in eight KTCN and eight non-KTCN corneas) was conducted first, after which the replication RNA-Seq experiment was performed on a second set of samples (17 KTCN and 17 non-KTCN corneas). Over 82% of the genes and almost 75% of the transcripts detected as differentially expressed in KTCN and non-KTCN corneas were confirmed in the replication study using another set of samples. We used these differentially expressed genes to generate a network of KTCN-deregulated genes. We found an extensive disruption of collagen synthesis and maturation pathways, as well as downregulation of the core elements of the TGF-β, Hippo, and Wnt signaling pathways influencing corneal organization. This first comprehensive transcriptome profiling of human KTCN corneas points further to a complex etiology of KTCN.