Label-free high-throughput microRNA expression profiling from total RNA

MicroRNAs (miRNAs) are key biological regulators and promising disease markers whose detection technologies hold great potentials in advancing fundamental research and medical diagnostics. Currently, miRNAs in biological samples have to be labeled before being applied to most high-throughput assays. Although effective, these labeling-based approaches are usually labor-intensive, time-consuming and liable to bias. Besides, the cross-hybridization of co-existing miRNA precursors (pre-miRNAs) is not adequately addressed in most assays that use total RNA as input. Here, we present a hybridization-triggered fluorescence strategy for label-free, microarray-based high-throughput miRNA expression profiling. The total RNA is directly applied to the microarray with a short fluorophore-linked oligonucleotide Universal Tag which can be selectively captured by the target-bound probes via base-stacking effects. This Stacking-Hybridized Universal Tag (SHUT) assay has been successfully used to analyze as little as 100 ng total RNA from human tissues, and found to be highly specific to homogenous miRNAs. Superb discrimination toward single-base mismatch at the 5' or 3' end has been demonstrated. Importantly, the pre-miRNAs generated negligible signals, validating the direct use of total RNA.     

Strategies for profiling microRNA expression

MicroRNAs (miRNAs) are a class of small RNAs ( approximately 22-nt) that play an important role in the control of different cell processes by negative regulation of protein-coding genes. In the last several years, a number of miRNA profiling strategies have been used to document the miRNA expression changes during physiological and pathological processes. Aberrant expression of miRNAs has been linked to developmental defects, cancer, neurological disorders, and heart diseases. Over 540 human miRNAs have been validated to date; however, computer models suggest there may be thousands more. As bench work continue to verify in silico predictions, miRNA profiling will remain a prominent tool for identification of differential expression miRNAs in normal cellular courses and human disorders. This review focuses on current strategies for miRNA expression profiling and discusses their sensitivity and specificity, as well as advantage and disadvantage.     

CAP-miRSeq: a comprehensive analysis pipeline for microRNA sequencing data

Background

miRNAs play a key role in normal physiology and various diseases. miRNA profiling through next generation sequencing (miRNA-seq) has become the main platform for biological research and biomarker discovery. However, analyzing miRNA sequencing data is challenging as it needs significant amount of computational resources and bioinformatics expertise. Several web based analytical tools have been developed but they are limited to processing one or a pair of samples at time and are not suitable for a large scale study. Lack of flexibility and reliability of these web applications are also common issues.

Results

We developed a Comprehensive Analysis Pipeline for microRNA Sequencing data (CAP-miRSeq) that integrates read pre-processing, alignment, mature/precursor/novel miRNA detection and quantification, data visualization, variant detection in miRNA coding region, and more flexible differential expression analysis between experimental conditions. According to computational infrastructure, users can install the package locally or deploy it in Amazon Cloud to run samples sequentially or in parallel for a large number of samples for speedy analyses. In either case, summary and expression reports for all samples are generated for easier quality assessment and downstream analyses. Using well characterized data, we demonstrated the pipeline’s superior performances, flexibility, and practical use in research and biomarker discovery.

Conclusions

CAP-miRSeq is a powerful and flexible tool for users to process and analyze miRNA-seq data scalable from a few to hundreds of samples. The results are presented in the convenient way for investigators or analysts to conduct further investigation and discovery.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2164-15-423) contains supplementary material, which is available to authorized users.     

Computational and analytical framework for small RNA profiling by high-throughput sequencing

The advent of high-throughput sequencing (HTS) methods has enabled direct approaches to quantitatively profile small RNA populations. However, these methods have been limited by several factors, including representational artifacts and lack of established statistical methods of analysis. Furthermore, massive HTS data sets present new problems related to data processing and mapping to a reference genome. Here, we show that cluster-based sequencing-by-synthesis technology is highly reproducible as a quantitative profiling tool for several classes of small RNA from Arabidopsis thaliana. We introduce the use of synthetic RNA oligoribonucleotide standards to facilitate objective normalization between HTS data sets, and adapt microarray-type methods for statistical analysis of multiple samples. These methods were tested successfully using mutants with small RNA biogenesis (miRNA-defective dcl1 mutant and siRNA-defective dcl2 dcl3 dcl4 triple mutant) or effector protein (ago1 mutant) deficiencies. Computational methods were also developed to rapidly and accurately parse, quantify, and map small RNA data.     

SUGAR: graphical user interface-based data refiner for high-throughput DNA sequencing

Background

Next-generation sequencers (NGSs) have become one of the main tools for current biology. To obtain useful insights from the NGS data, it is essential to control low-quality portions of the data affected by technical errors such as air bubbles in sequencing fluidics.

Results

We develop a software SUGAR (subtile-based GUI-assisted refiner) which can handle ultra-high-throughput data with user-friendly graphical user interface (GUI) and interactive analysis capability. The SUGAR generates high-resolution quality heatmaps of the flowcell, enabling users to find possible signals of technical errors during the sequencing. The sequencing data generated from the error-affected regions of a flowcell can be selectively removed by automated analysis or GUI-assisted operations implemented in the SUGAR. The automated data-cleaning function based on sequence read quality (Phred) scores was applied to a public whole human genome sequencing data and we proved the overall mapping quality was improved.

Conclusion

The detailed data evaluation and cleaning enabled by SUGAR would reduce technical problems in sequence read mapping, improving subsequent variant analysis that require high-quality sequence data and mapping results. Therefore, the software will be especially useful to control the quality of variant calls to the low population cells, e.g., cancers, in a sample with technical errors of sequencing procedures.     

Integrating high-throughput microRNA and mRNA expression data to identify risk mRNA signature for pancreatic cancer prognosis

Pancreatic cancer is a malignancy of the digestive system characterized by poor prognosis. A number of prognostic messenger RNA (mRNA) signatures have been identified by using the high-throughput expression profiles. MicroRNAs (miRNA) play a critical role in regulating multiple cellular functions. However, no such integrated analysis of miRNAs and mRNAs for studying the prognostic mechanisms of pancreatic cancer has been reported. In this study, we first identified prognostic mRNAs and miRNAs based on The Cancer Genome Atlas datasets, and then performed an enrichment analysis to explore the underlying biological mechanisms involved in pancreatic cancer prognosis at the mRNA level. Furthermore, we performed an integrated analysis of mRNAs and miRNAs to identify prognostic subpathways, which were closely associated with pancreatic cancer genes and tumor hallmarks and involved in hypoxia, oxidative phosphyorylation and xenobiotic metabolisms. Meanwhile, we performed a random walk algorithm based on global network, prognostic mRNAs and miRNAs, and identified top risk mRNAs as the prognostic signature. Finally, an independent testing set was used to confirm the predictive power of the top mRNA signature, and most of these genes involved were known oncogenes. In conclusion, we performed a series of integrated analyses by comprehensively exploring pancreatic cancer prognosis and systematically optimized the prognostic signature for clinical use.     

Extensive profiling of a complex microbial community by high-throughput sequencing

Complex microbial communities remain poorly characterized despite their ubiquity and importance to human and animal health, agriculture, and industry. Attempts to describe microbial communities by either traditional microbiological methods or molecular methods have been limited in both scale and precision. The availability of genomics technologies offers an unprecedented opportunity to conduct more comprehensive characterizations of microbial communities. Here we describe the application of an established molecular diagnostic method based on the chaperonin-60 sequence, in combination with high-throughput sequencing, to the profiling of a microbial community: the pig intestinal microbial community. Four libraries of cloned cpn60 sequences were generated by two genomic DNA extraction procedures in combination with two PCR protocols. A total of 1,125 cloned cpn60 sequences from the four libraries were sequenced. Among the 1,125 cloned cpn60 sequences, we identified 398 different nucleotide sequences encoding 280 unique peptide sequences. Pairwise comparisons of the 398 unique nucleotide sequences revealed a high degree of sequence diversity within the library. Identification of the likely taxonomic origins of cloned sequences ranged from imprecise, with clones assigned to a taxonomic subclass, to precise, for cloned sequences with 100% DNA sequence identity with a species in our reference database. The compositions of the four libraries were compared and differences related to library construction parameters were observed. Our results indicate that this method is an alternative to 16S rRNA sequence-based studies which can be scaled up for the purpose of performing a potentially comprehensive assessment of a given microbial community or for comparative studies.     

THetA: inferring intra-tumor heterogeneity from high-throughput DNA sequencing data

Tumor samples are typically heterogeneous, containing admixture by normal, non-cancerous cells and one or more subpopulations of cancerous cells. Whole-genome sequencing of a tumor sample yields reads from this mixture, but does not directly reveal the cell of origin for each read. We introduce THetA (Tumor Heterogeneity Analysis), an algorithm that infers the most likely collection of genomes and their proportions in a sample, for the case where copy number aberrations distinguish subpopulations. THetA successfully estimates normal admixture and recovers clonal and subclonal copy number aberrations in real and simulated sequencing data. THetA is available at http://compbio.cs.brown.edu/software/.     

Quantifying selection in high-throughput Immunoglobulin sequencing data sets

High-throughput immunoglobulin sequencing promises new insights into the somatic hypermutation and antigen-driven selection processes that underlie B-cell affinity maturation and adaptive immunity. The ability to estimate positive and negative selection from these sequence data has broad applications not only for understanding the immune response to pathogens, but is also critical to determining the role of somatic hypermutation in autoimmunity and B-cell cancers. Here, we develop a statistical framework for Bayesian estimation of Antigen-driven SELectIoN (BASELINe) based on the analysis of somatic mutation patterns. Our approach represents a fundamental advance over previous methods by shifting the problem from one of simply detecting selection to one of quantifying selection. Along with providing a more intuitive means to assess and visualize selection, our approach allows, for the first time, comparative analysis between groups of sequences derived from different germline V(D)J segments. Application of this approach to next-generation sequencing data demonstrates different selection pressures for memory cells of different isotypes. This framework can easily be adapted to analyze other types of DNA mutation patterns resulting from a mutator that displays hot/cold-spots, substitution preference or other intrinsic biases.     

Detecting non-allelic homologous recombination from high-throughput sequencing data

Non-allelic homologous recombination (NAHR) is a common mechanism for generating genome rearrangements and is implicated in numerous genetic disorders, but its detection in high-throughput sequencing data poses a serious challenge. We present a probabilistic model of NAHR and demonstrate its ability to find NAHR in low-coverage sequencing data from 44 individuals. We identify NAHR-mediated deletions or duplications in 109 of 324 potential NAHR loci in at least one of the individuals. These calls segregate by ancestry, are more common in closely spaced repeats, often result in duplicated genes or pseudogenes, and affect highly studied genes such as GBA and CYP2E1.

Electronic supplementary material

The online version of this article (doi:10.1186/s13059-015-0633-1) contains supplementary material, which is available to authorized users.