Comparative analysis of human protein-coding and noncoding RNAs between brain and 10 mixed cell lines by RNA-Seq

Artificial Intelligence Predictor for Alzheimer’s Disease Trained on Blood Transcriptome: The Role of Oxidative Stress

Alzheimer’s disease (AD) is an incurable neurodegenerative disease diagnosed by clinicians through healthcare records and neuroimaging techniques. These methods lack sensitivity and specificity, so new antemortem non-invasive strategies to diagnose AD are needed. Herein, we designed a machine learning predictor based on transcriptomic data obtained from the blood of AD patients and individuals without dementia (non-AD) through an 8 × 60 K microarray. The dataset was used to train different models with different hyperparameters. The support vector machines method allowed us to reach a Receiver Operating Characteristic score of 93% and an accuracy of 89%. High score levels were also achieved by the neural network and logistic regression methods. Furthermore, the Gene Ontology enrichment analysis of the features selected to train the model along with the genes differentially expressed between the non-AD and AD transcriptomic profiles shows the “mitochondrial translation” biological process to be the most interesting. In addition, inspection of the KEGG pathways suggests that the accumulation of β-amyloid triggers electron transport chain impairment, enhancement of reactive oxygen species and endoplasmic reticulum stress. Taken together, all these elements suggest that the oxidative stress induced by β-amyloid is a key feature trained by the model for the prediction of AD with high accuracy.

Identification of Long Non-coding RNA Isolated From Naturally Infected Macrophages and Associated With Bovine Johne's Disease in Canadian Holstein Using a Combination of Neural Networks and Logistic Regression

Mycobacterium avium ssp. paratuberculosis (MAP) causes chronic enteritis in most ruminants. The pathogen MAP causes Johne's disease (JD), a chronic, incurable, wasting disease. Weight loss, diarrhea, and a gradual drop in milk production characterize the disease's clinical phase, culminating in death. Several studies have characterized long non-coding RNA (lncRNA) in bovine tissues, and a previous study characterizes (lncRNA) in macrophages infected with MAP in vitro. In this study, we aim to characterize the lncRNA in macrophages from cows naturally infected with MAP. From 15 herds, feces and blood samples were collected for each cow older than 24 months, twice yearly over 3–5 years. Paired samples were analyzed by fecal PCR and blood ELISA. We used RNA-seq data to study lncRNA in macrophages from 33 JD(+) and 33 JD(–) dairy cows. We performed RNA-seq analysis using the “new Tuxedo” suite. We characterized lncRNA using logistic regression and multilayered neural networks and used DESeq2 for differential expression analysis and Panther and Reactome classification systems for gene ontology (GO) analysis. The study identified 13,301 lncRNA, 605 of which were novel lncRNA. We found seven genes close to differentially expressed lncRNA, including CCDC174, ERI1, FZD1, TWSG1, ZBTB38, ZNF814, and ZSCAN4. None of the genes associated with susceptibility to JD have been cited in the literature. LncRNA target genes were significantly enriched for biological process GO terms involved in immunity and nucleic acid regulation. These include the MyD88 pathway (TLR5), GO:0043312 (neutrophil degranulation), GO:0002446 (neutrophil-mediated immunity), and GO:0042119 (neutrophil activation). These results identified lncRNA with potential roles in host immunity and potential candidate genes and pathways through which lncRNA might function in response to MAP infection.

Expression of BANCR promotes papillary thyroid cancer by targeting thyroid stimulating hormone receptor

Papillary thyroid carcinoma (PTC) is the most common form of non-medullary thyroid cancer, accounting for ~80% of all cases of thyroid cancer. The aim of the present study was to explore the role of BRAF-activated long noncoding RNA (BANCR) in the development of PTC. Using reverse transcription-quantitative polymerase chain reaction (RT-qPCR), the mRNA expression levels of BANCR, thyroid-stimulating hormone receptor (TSHR) and cyclin D1 between PTC and benign control thyroid nodule tissue samples from 60 patients were determined. Using RT-qPCR and western blot analysis, the expression levels of TSHR and cyclin D1 mRNA and protein were determined in cells transfected with BANCR-small interfering (si)RNA. An MTT assay and flow cytometry were used to analyze the effect of BANCR knockdown on the proliferation and cell cycle distribution of IHH-4 PTC cells. The expression of BANCR, TSHR and cyclin D1 was increased in the PTC group compared with the control group based on the RT-qPCR data. The transfection of IHH-4 cells with BANCR-siRNA induced the inhibition of TSHR and cyclin D1 expression compared with a transfection control. In addition, the proliferation of the IHH-4 cells transfected with BANCR-siRNA was suppressed, relative to the transfection control, and cells arrested in the G0/G1 phase, potentially due to the inhibition of the expression of cyclin D1. The data suggested that the expression of BANCR may promote the development of malignant thyroid nodules via the modulation of TSHR expression and its downstream effector, cyclin D1.

LncRNAs in Secondary Hair Follicle of Cashmere Goat: Identification, Expression, and Their Regulatory Network in Wnt Signaling Pathway

Long noncoding RNAs (lncRNAs) are a novel class of eukaryotic transcripts. They are thought to act as a critical regulator of protein-coding gene expression. Herein, we identified and characterized 13 putative lncRNAs from the expressed sequence tags from secondary hair follicle of Cashmere goat. Furthermore, we investigated their transcriptional pattern in secondary hair follicle of Liaoning Cashmere goat during telogen and anagen phases. Also, we generated intracellular regulatory networks of upregulated lncRNAs at anagen in Wnt signaling pathway based on bioinformatics analysis. The relative expression of six putative lncRNAs (lncRNA-599618, -599556, -599554, -599547, -599531, and -599509) at the anagen phase is significantly higher than that at telogen. Compared with anagen, the relative expression of four putative lncRNAs (lncRNA-599528, -599518, -599511, and -599497) was found to be significantly upregulated at telogen phase. The network generated showed that a rich and complex regulatory relationship of the putative lncRNAs and related miRNAs with their target genes in Wnt signaling pathway. Our results from the present study provided a foundation for further elucidating the functional and regulatory mechanisms of these putative lncRNAs in the development of secondary hair follicle and cashmere fiber growth of Cashmere goat.

lncRNAs are associated with polysomes during adipose-derived stem cell differentiation

Over the past few years, an increasing number of long noncoding RNAs (lncRNAs) have been identified in mammalian genomes. Most of these lncRNAs are expressed at low levels in different human cell types. lncRNAs are found not only in the nucleus but are also enriched in the cytosolic fraction and are associated with translating polysomes. Expression of lncRNAs that have putative roles in cell differentiation has been identified in embryonic and adult stem cells. Nevertheless, the mechanisms by which lncRNAs operate in the cell are still poorly understood.Here, we studied the expression of the subpopulation of lncRNAs that are associated with polysomes in adipose-derived stem cells (hASCs) during their commitment to adipocytes. We established that lncRNAs and protein coding genes have similar expression levels. The relatively comparable expression of these transcripts could be a particular feature of hASCs. We then show that lncRNAs are associated with polysomes in undifferentiated and early differentiating cells, which was confirmed by quantitative RT-PCR. The association of lncRNAs with polysomes was also comparable to that of mRNAs. Our results suggest that the presence of lncRNAs in the polysomal RNA fraction is not the result of random association. We observed that a high percentage of lncRNAs are actively mobilized to or from polysomes during early stages of adipogenesis. Moreover, we found several lncRNAs that can potentially target miRNAs relevant to adipogenesis.

Characterizing and annotating the genome using RNA-seq data

Bioinformatics methods for various RNA-seq data analyses are in fast evolution with the improvement of sequencing technologies. However, many challenges still exist in how to efficiently process the RNA-seq data to obtain accurate and comprehensive results. Here we reviewed the strategies for improving diverse transcriptomic studies and the annotation of genetic variants based on RNA-seq data. Mapping RNA-seq reads to the genome and transcriptome represent two distinct methods for quantifying the expression of genes/transcripts. Besides the known genes annotated in current databases, many novel genes/transcripts (especially those long noncoding RNAs) still can be identified on the reference genome using RNA-seq. Moreover, owing to the incompleteness of current reference genomes, some novel genes are missing from them. Genome- guided and de novo transcriptome reconstruction are two effective and complementary strategies for identifying those novel genes/transcripts on or beyond the reference genome. In addition, integrating the genes of distinct databases to conduct transcriptomics and genetics studies can improve the results of corresponding analyses.

A catalogue of novel bovine long noncoding RNA across 18 tissues

Long non-coding RNA (lncRNA) have been implicated in diverse biological roles including gene regulation and genomic imprinting. Identifying lncRNA in bovine across many differing tissue would contribute to the current repertoire of bovine lncRNA, and help further improve our understanding of the evolutionary importance and constraints of these transcripts. Additionally, it could aid in identifying sites in the genome outside of protein coding genes where mutations could contribute to variation in complex traits. This is particularly important in bovine as genomic predictions are increasingly used in genetic improvement for milk and meat production. Our aim was to identify and annotate novel long non coding RNA transcripts in the bovine genome captured from RNA Sequencing (RNA-Seq) data across 18 tissues, sampled in triplicate from a single cow. To address the main challenge in identifying lncRNA, namely distinguishing lncRNA transcripts from unannotated genes and protein coding genes, a lncRNA identification pipeline with a number of filtering steps was developed. A total of 9,778 transcripts passed the filtering pipeline. The bovine lncRNA catalogue includes MALAT1 and HOTAIR, both of which have been well described in human and mouse genomes. We attempted to validate the lncRNA in libraries from three additional cows. 726 (87.47%) liver and 1,668 (55.27%) blood class 3 lncRNA were validated with stranded liver and blood libraries respectively. Additionally, this study identified a large number of novel unknown transcripts in the bovine genome with high protein coding potential, illustrating a clear need for better annotations of protein coding genes.

The long noncoding RNA LINC01207 promotes proliferation of lung adenocarcinoma

Lung adenocarcinoma (LAD) and lung squamous cell cancer (LSCC) are two most common histological types of lung cancer, while they differ in many aspects. Recent evidence shows that long non-coding RNAs (lncRNAs) play an important role in the process of cancer initiation and progression. Thus, characterization of LAD and LSCC associated lncRNAs may help understand the difference between LAD and LSCC. Here, we analyzed three sets of RNA-seq data, including LAD RNA-seq data from TCGA project. We identified a novel lncRNA, long intergenic non-protein coding RNA 1207 (LINC01207) which was significantly up-regulated in LAD tissues compared with paired non-tumor tissues (5.78 fold increase, P<0.05), while there was no significant differences between LSCC tissues and adjacent non-tumor tissues. The expression level of LINC01207 was associated with TNM stage of LAD patients, and higher LINC01207 level indicated advanced TNM stage (P<0.05) and shorter survival (HR=2.53, P<0.05). By small interfering RNA (siRNA) mediated knockdown of LINC01207, we determined the biological function of LINC01207 in A549 cell line. After knockdown of LINC01207, cell proliferation ability was inhibited. Further analysis showed that after silence of LINC01207, the percentage of apoptotic cells significantly increased. By RNA immunoprecipitation and Chromatin immunoprecipitation assay, we demonstrated that LINC01207 could bind with EZH2 and mediated trimethylation of histone 3 lysine 27 at the promoter region of Bad, an important pro-apoptotic gene. Finally, we developed xenograft tumor models in nude mice and xenograft tumors derived from A549 cells transfected with siRNA-LINC01207 had significantly lower tumor weight and smaller tumor volume. In summary, the novel lncRNA, LINC01207 is specifically up-regulated in LAD but not in LSCC; and LINC01207 could promote LAD cell growth both in vivo and in vitro.

RNAs specifically affect gene expression in a length, position and sequence dependent manner

We aim to explore if RNA regulating gene expression is affected by length, sequence and position of RNA. HeLa cells were co-transfected with modulator plasmids (derived from pcDNA3.1 vector containing different length regulating sequences that produce RNAs) and reporter plasmids (derived from pEGFP-C1 vector); In addition, HeLa cells were transfected with plasmids that possess different sequences of downstream or adjacent genes of GFP reporter gene. We found that long inserting sequences of modulator plasmids induced stronger GFP gene activation than short inserting sequences. Changing of downstream sequences of GFP gene induced significant effects on GFP gene expression. Short sequences of adjacent genes of GFP activated GFP gene. Bioinformatics analysis of genes which is highly expressed in differentiating cells (thymocyte cells, germinal center B-cells) and quiescent cells (T cells, B cells) shows that differentiating cells produce longer RNA than quiescent cells. These findings demonstrate that the length, sequence and producing position of RNAs are important factors for RNA regulating gene expression.

A genetic program theory of aging using an RNA population model

Aging is a common characteristic of multicellular eukaryotes. Copious hypotheses have been proposed to explain the mechanisms of aging, but no single theory is generally acceptable. In this article, we refine the RNA population gene activating model (Lv et al., 2003) based on existing reports as well as on our own latest findings. We propose the RNA population model as a genetic theory of aging. The new model can also be applied to differentiation and tumorigenesis and could explain the biological significance of non-coding DNA, RNA, and repetitive sequence DNA. We provide evidence from the literature as well as from our own findings for the roles of repetitive sequences in gene activation. In addition, we predict several phenomena related to aging and differentiation based on this model.

Identification of novel transcripts and noncoding RNAs in bovine skin by deep next generation sequencing

BACKGROUND

Deep RNA sequencing (RNAseq) has opened a new horizon for understanding global gene expression. The functional annotation of non-model mammalian genomes including bovines is still poor compared to that of human and mouse. This particularly applies to tissues without direct significance for milk and meat production, like skin, in spite of its multifunctional relevance for the individual. Thus, applying an RNAseq approach, we performed a whole transcriptome analysis of pigmented and nonpigmented bovine skin to describe the comprehensive transcript catalogue of this tissue.

RESULTS

A total of 39,577 unique primary skin transcripts were mapped to the bovine reference genome assembly. The majority of the transcripts were mapped to known transcriptional units (65%). In addition to the reannotation of known genes, a substantial number (10,884) of unknown transcripts (UTs) were discovered, which had not previously been annotated. The classification of UTs was based on the prediction of their coding potential and comparative sequence analysis, subsequently followed by meticulous manual curation. The classification analysis and experimental validation of selected UTs confirmed that RNAseq data can be used to amend the annotation of known genes by providing evidence for additional exons, untranslated regions or splice variants, by approving genes predicted in silico and by identifying novel bovine loci. A large group of UTs (4,848) was predicted to potentially represent long noncoding RNA (lncRNA). Predominantly, potential lncRNAs mapped in intergenic chromosome regions (4,365) and therefore, were classified as potential intergenic lncRNA. Our analysis revealed that only about 6% of all UTs displayed interspecies conservation and discovered a variety of unknown transcripts without interspecies homology but specific expression in bovine skin.

CONCLUSIONS

The results of our study demonstrate a complex transcript pattern for bovine skin and suggest a possible functional relevance of novel transcripts, including lncRNA, in the modulation of pigmentation processes. The results also indicate that the comprehensive identification and annotation of unknown transcripts from whole transcriptome analysis using RNAseq data remains a tremendous future challenge.

CNCTDiscriminator: coding and noncoding transcript discriminator - an excursion through hypothesis learning and ensemble learning approaches

The statistics about the open reading frames, the base compositions and the properties of the predicted secondary structures have potential to address the problem of discriminating coding and noncoding transcripts. Again, the Next Generation Sequencing platform, RNA-seq, provides us bounty of data from which expression profiles of the transcripts can be extracted which urged us adding a new set of dimension in this classification task. In this paper, we proposed CNCTDiscriminator -- a coding and noncoding transcript discriminating system where we applied the integration of these four categories of features about the transcripts. The feature integration was done using both hypothesis learning and feature specific ensemble learning approaches. The CNCTDiscriminator model which was trained with composition and ORF features outperforms (precision 83.86%, recall 82.01%) other three popular methods -- CPC (precision 98.31%, recall 25.95%), CPAT (precision 97.74%, recall 52.50%) and PORTRAIT (precision 84.37%, recall 73.2%) when applied to an independent benchmark dataset. However, the CNCTDiscriminator model that was trained using the ensemble approach shows comparable performance (precision 89.85%, recall 71.08%).

Genomics and proteomics in solving brain complexity

The human brain is extraordinarily complex, composed of billions of neurons and trillions of synaptic connections. Neurons are organized into circuit assemblies that are modulated by specific interneurons and non-neuronal cells, such as glia and astrocytes. Data on human genome sequences predicts that each of these cells in the human brain has the potential of expressing ∼20 000 protein coding genes and tens of thousands of noncoding RNAs. A major challenge in neuroscience is to determine (1) how individual neurons and circuitry utilize this potential during development and maturation of the nervous system, and for higher brain functions such as cognition, and (2) how this potential is altered in neurological and psychiatric disorders. In this review, we will discuss how recent advances in next generation sequencing, proteomics and bioinformatics have transformed our understanding of gene expression and the functions of neural circuitry, memory storage, and disorders of cognition.

Dissecting the Characteristics and Dynamics of Human Protein Complexes at Transcriptome Cascade Using RNA-Seq Data

Human protein complexes play crucial roles in various biological processes as the functional module. However, the expression features of human protein complexes at the transcriptome cascade are poorly understood. Here, we used the RNA-Seq data from 16 disparate tissues and four types of human cancers to explore the characteristics and dynamics of human protein complexes. We observed that many individual components of human protein complexes can be generated by multiple distinct transcripts. Similar with yeast, the human protein complex constituents are inclined to co-express in diverse tissues. The dominant isoform of the genes involved in protein complexes tend to encode the complex constituents in each tissue. Our results indicate that the protein complex dynamics not only correlate with the presence or absence of complexes, but may also be related to the major isoform switching for complex subunits. Between any two cancers of breast, colon, lung and prostate, we found that only a few of the differentially expressed transcripts associated with complexes were identical, but 5-10 times more protein complexes involved in differentially expressed transcripts were common. Collectively, our study reveals novel properties and dynamics of human protein complexes at the transcriptome cascade in diverse normal tissues and different cancers.

cDNA normalization by hydroxyapatite chromatography to enrich transcriptome diversity in RNA-seq applications

Second-generation sequencing (SGS) has become the preferred method for RNA transcriptome profiling of organisms and single cells. However, SGS analysis of transcriptome diversity (including protein-coding transcripts and regulatory non-coding RNAs) is inefficient unless the sample of interest is first depleted of nucleic acids derived from ribosomal RNA (rRNA), which typically account for up to 95% of total intracellular RNA content. Here we describe a novel microscale hydroxyapatite chromatography (HAC) normalization method to remove eukaryotic and prokaryotic high abundant rRNA species, thereby increasing sequence coverage depth and transcript diversity across non-rRNA populations. RNA-seq analysis of Escherichia coli K-12 and human intracellular total RNA showed that HAC-based normalization enriched for all non-ribosomal RNA species regardless of RNA transcript abundance or length when compared with untreated controls. Microcolumn HAC normalization generated rRNA-depleted cDNA libraries comparable to the well-established duplex specific nuclease (DSN) normalization and Ribo-Zero rRNA-depletion methods, thus establishing microscale HAC as an effective, cost saving, and non-destructive alternative normalization technique.

The baboon kidney transcriptome: analysis of transcript sequence, splice variants, and abundance

The baboon is an invaluable model for the study of human health and disease, including many complex diseases of the kidney. Although scientists have made great progress in developing this animal as a model for numerous areas of biomedical research, genomic resources for the baboon, such as a quality annotated genome, are still lacking. To this end, we characterized the baboon kidney transcriptome using high-throughput cDNA sequencing (RNA-Seq) to identify genes, gene variants, single nucleotide polymorphisms (SNPs), insertion-deletion polymorphisms (InDels), cellular functions, and key pathways in the baboon kidney to provide a genomic resource for the baboon. Analysis of our sequencing data revealed 45,499 high-confidence SNPs and 29,813 InDels comparing baboon cDNA sequences with the human hg18 reference assembly and identified 35,900 cDNAs in the baboon kidney, including 35,150 transcripts representing 15,369 genic genes that are novel for the baboon. Gene ontology analysis of our sequencing dataset also identified numerous biological functions and canonical pathways that were significant in the baboon kidney, including a large number of metabolic pathways that support known functions of the kidney. The results presented in this study catalogues the transcribed mRNAs, noncoding RNAs, and hypothetical proteins in the baboon kidney and establishes a genomic resource for scientists using the baboon as an experimental model.

Long noncoding RNAs in development and disease of the central nervous system

The central nervous system (CNS) is a complex biological system composed of numerous cell types working in concert. The intricate development and functioning of this highly ordered structure depends upon exquisite spatial and temporal control of gene expression in the cells comprising the CNS. Thus, gene regulatory networks that control cell fates and functions play critical roles in the CNS. Failure to develop and maintain intricate regulatory networks properly leads to impaired development or neural dysfunction, which might manifest as neurological disorders. Long noncoding RNAs (lncRNAs) are emerging as important components of gene regulatory networks, working in concert with transcription factors and epigenetic regulators of gene expression. Interestingly, many lncRNAs are highly expressed in the adult and developing brain, often showing precise temporal and spatial patterns of expression. This specificity of expression and growing awareness of the importance of lncRNAs suggest that they play key roles in CNS development and function. In this review, we highlight the growing evidence for the importance of lncRNAs in the CNS and the indications that their dysregulation underlies some neurological disorders.

Incorporating the human gene annotations in different databases significantly improved transcriptomic and genetic analyses

Human gene annotation is crucial for conducting transcriptomic and genetic studies; however, the impacts of human gene annotations in diverse databases on related studies have been less evaluated. To enable full use of various human annotation resources and better understand the human transcriptome, here we systematically compare the human annotations present in RefSeq, Ensembl (GENCODE), and AceView on diverse transcriptomic and genetic analyses. We found that the human gene annotations in the three databases are far from complete. Although Ensembl and AceView annotated more genes than RefSeq, more than 15,800 genes from Ensembl (or AceView) are within the intergenic and intronic regions of AceView (or Ensembl) annotation. The human transcriptome annotations in RefSeq, Ensembl, and AceView had distinct effects on short-read mapping, gene and isoform expression profiling, and differential expression calling. Furthermore, our findings indicate that the integrated annotation of these databases can obtain a more complete gene set and significantly enhance those transcriptomic analyses. We also observed that many more known SNPs were located within genes annotated in Ensembl and AceView than in RefSeq. In particular, 1033 of 3041 trait/disease-associated SNPs involved in about 200 human traits/diseases that were previously reported to be in RefSeq intergenic regions could be relocated within Ensembl and AceView genes. Our findings illustrate that a more complete transcriptome generated by incorporating human gene annotations in diverse databases can strikingly improve the overall results of transcriptomic and genetic studies.

Pathway analysis of the human brain transcriptome in disease

Pathway analysis is a powerful method for discerning differentially regulated genes and elucidating their biological importance. It allows for the identification of perturbed or aberrantly expressed genes within a biological context from extensive data sets and offers a simplistic approach for interrogating such data sets. With the growing use of microarrays and RNA-Seq, data for genome-wide studies are growing at an alarming rate, and the use of deep sequencing is revealing elements of the genome previously uncharacterised. Through the employment of pathway analysis, mechanisms in complex diseases may be explored and novel causatives found primarily through differentially regulated genes. Further, with the implementation of next generation sequencing, a deeper resolution may be attained, particularly in identification of isoform diversity and SNPs. Here, we look at a broad overview of pathway analysis in the human brain transcriptome and its relevance in teasing out underlying causes of complex diseases. We will outline processes in data gathering and analysis of particular diseases in which these approaches have been successful.

Long non-coding RNAs in stem cell pluripotency

Pluripotency refers to the self-renewal of undifferentiated embryonic stem cells (ESCs), and is maintained by a tightly regulated gene regulatory network involving an intricate interplay between transcription factors and their genomic targets, as well as epigenetic processes that influence gene expression. Long non-coding RNAs (lncRNAs) are newly discovered members of gene regulatory networks that govern a variety of cell functions. Defined as RNA transcripts larger than 200 nucleotides, lncRNAs have little or no protein-coding capacity and have been shown to act via various mechanisms, and are important in a variety of biological functions. Recent reports have described the discovery of pluripotent lncRNAs involved in the maintenance and induction of stem cell pluripotency. Here, we discuss how lncRNAs may integrate into the pluripotency network, as well as prominent questions in this emerging field.

De novo transcriptome assembly of RNA-Seq reads with different strategies

De novo transcriptome assembly is an important approach in RNA-Seq data analysis and it can help us to reconstruct the transcriptome and investigate gene expression profiles without reference genome sequences. We carried out transcriptome assemblies with two RNA-Seq datasets generated from human brain and cell line, respectively. We then determined an efficient way to yield an optimal overall assembly using three different strategies. We first assembled brain and cell line transcriptome using a single k-mer length. Next we tested a range of values of k-mer length and coverage cutoff in assembling. Lastly, we combined the assembled contigs from a range of k values to generate a final assembly. By comparing these assembly results, we found that using only one k-mer value for assembly is not enough to generate good assembly results, but combining the contigs from different k-mer values could yield longer contigs and greatly improve the overall assembly.

Overview of available methods for diverse RNA-Seq data analyses

RNA-Seq technology is becoming widely used in various transcriptomics studies; however, analyzing and interpreting the RNA-Seq data face serious challenges. With the development of high-throughput sequencing technologies, the sequencing cost is dropping dramatically with the sequencing output increasing sharply. However, the sequencing reads are still short in length and contain various sequencing errors. Moreover, the intricate transcriptome is always more complicated than we expect. These challenges proffer the urgent need of efficient bioinformatics algorithms to effectively handle the large amount of transcriptome sequencing data and carry out diverse related studies. This review summarizes a number of frequently-used applications of transcriptome sequencing and their related analyzing strategies, including short read mapping, exon-exon splice junction detection, gene or isoform expression quantification, differential expression analysis and transcriptome reconstruction.