Scalable transcriptome preparation for massive parallel sequencing

Background

The tremendous output of massive parallel sequencing technologies requires automated robust and scalable sample preparation methods to fully exploit the new sequence capacity.

Methodology

In this study, a method for automated library preparation of RNA prior to massively parallel sequencing is presented. The automated protocol uses precipitation onto carboxylic acid paramagnetic beads for purification and size selection of both RNA and DNA. The automated sample preparation was compared to the standard manual sample preparation.

Conclusion/Significance

The automated procedure was used to generate libraries for gene expression profiling on the Illumina HiSeq 2000 platform with the capacity of 12 samples per preparation with a significantly improved throughput compared to the standard manual preparation. The data analysis shows consistent gene expression profiles in terms of sensitivity and quantification of gene expression between the two library preparation methods.     

Optimizing a massive parallel sequencing workflow for quantitative miRNA expression analysis

BACKGROUND: Massive Parallel Sequencing methods (MPS) can extend and improve the knowledge obtained by conventional microarray technology, both for mRNAs and short non-coding RNAs, e.g. miRNAs. The processing methods used to extract and interpret the information are an important aspect of dealing with the vast amounts of data generated from short read sequencing. Although the number of computational tools for MPS data analysis is constantly growing, their strengths and weaknesses as part of a complex analytical pipe-line have not yet been well investigated. PRIMARY FINDINGS: A benchmark MPS miRNA dataset, resembling a situation in which miRNAs are spiked in biological replication experiments was assembled by merging a publicly available MPS spike-in miRNAs data set with MPS data derived from healthy donor peripheral blood mononuclear cells. Using this data set we observed that short reads counts estimation is strongly under estimated in case of duplicates miRNAs, if whole genome is used as reference. Furthermore, the sensitivity of miRNAs detection is strongly dependent by the primary tool used in the analysis. Within the six aligners tested, specifically devoted to miRNA detection, SHRiMP and MicroRazerS show the highest sensitivity. Differential expression estimation is quite efficient. Within the five tools investigated, two of them (DESseq, baySeq) show a very good specificity and sensitivity in the detection of differential expression. CONCLUSIONS: The results provided by our analysis allow the definition of a clear and simple analytical optimized workflow for miRNAs digital quantitative analysis.     

Decoding a substantial set of samples in parallel by massive sequencing

There has been a dramatic increase of throughput of sequenced bases in the last years but sequencing a multitude of samples in parallel has not yet developed equally. Here we present a novel strategy where the combination of two tags is used to link sequencing reads back to their origins from a pool of samples. By incorporating the tags in two steps sample-handling complexity is lowered by nearly 100 times compared to conventional indexing protocols. In addition, the method described here enables accurate identification and typing of thousands of samples in parallel. In this study the system was designed to test 4992 samples using only 122 tags. To prove the concept of the two-tagging method, the highly polymorphic 2(nd) exon of DLA-DRB1 in dogs and wolves was sequenced using the 454 GS FLX Titanium Chemistry. By requiring a minimum sequence depth of 20 reads per sample, 94% of the successfully amplified samples were genotyped. In addition, the method allowed digital detection of chimeric fragments. These results demonstrate that it is possible to sequence thousands of samples in parallel without complex pooling patterns or primer combinations. Furthermore, the method is highly scalable as only a limited number of additional tags leads to substantial increase of the sample size.     

Minor variant detection in amplicons using 454 massive parallel pyrosequencing: experiences and considerations for successful applications

Ultra-deep sequencing (UDS) of amplicons is a major application for next-generation sequencing technologies, even more so for the 454 Genome Sequencer FLX. Especially for this application, errors that might be introduced during any of the sample processing or data analysis steps should be avoided or at least recognized, as they might lead to aberrant sequence variant calling. Since 454 pyrosequencing relies on PCR-driven target amplification, it is key to differentiate errors introduced during the amplification step from genuine minority variants. Thereto, optimal primer design is imperative because primer selection, primer dimer formation, and nonspecific binding may all affect the quality and outcome of amplicon-based deep sequencing. Also, other intrinsic PCR characteristics including amplification drift and the formation of secondary structures may influence sequencing data quality. We illustrate these phenomena using real life case studies and propose experimental and analytical evidence-based solutions for effective practice. Furthermore, because accuracy of the DNA polymerase is vital for reliable UDS results, a comparative analysis of error profiles from seven different DNA polymerases was performed and experimentally assessed in parallel by 454 sequencing. Finally, intra and interrun variability evaluation of the 454 sequencing protocol revealed highly reproducible results in amplicon-based UDS.     

ROVER variant caller: read-pair overlap considerate variant-calling software applied to PCR-based massively parallel sequencing datasets

Background

We recently described Hi-Plex, a highly multiplexed PCR-based target-enrichment system for massively parallel sequencing (MPS), which allows the uniform definition of library size so that subsequent paired-end sequencing can achieve complete overlap of read pairs. Variant calling from Hi-Plex-derived datasets can thus rely on the identification of variants appearing in both reads of read-pairs, permitting stringent filtering of sequencing chemistry-induced errors. These principles underly ROVER software (derived from Read Overlap PCR-MPS variant caller), which we have recently used to report the screening for genetic mutations in the breast cancer predisposition gene PALB2. Here, we describe the algorithms underlying ROVER and its usage.

Results

ROVER enables users to quickly and accurately identify genetic variants from PCR-targeted, overlapping paired-end MPS datasets. The open-source availability of the software and threshold tailorability enables broad access for a range of PCR-MPS users.

Methods

ROVER is implemented in Python and runs on all popular POSIX-like operating systems (Linux, OS X). The software accepts a tab-delimited text file listing the coordinates of the target-specific primers used for targeted enrichment based on a specified genome-build. It also accepts aligned sequence files resulting from mapping to the same genome-build. ROVER identifies the amplicon a given read-pair represents and removes the primer sequences by using the mapping co-ordinates and primer co-ordinates. It considers overlapping read-pairs with respect to primer-intervening sequence. Only when a variant is observed in both reads of a read-pair does the signal contribute to a tally of read-pairs containing or not containing the variant. A user-defined threshold informs the minimum number of, and proportion of, read-pairs a variant must be observed in for a ‘call’ to be made. ROVER also reports the depth of coverage across amplicons to facilitate the identification of any regions that may require further screening.

Conclusions

ROVER can facilitate rapid and accurate genetic variant calling for a broad range of PCR-MPS users.     

Genome-wide footprints of pig domestication and selection revealed through massive parallel sequencing of pooled DNA

Background

Artificial selection has caused rapid evolution in domesticated species. The identification of selection footprints across domesticated genomes can contribute to uncover the genetic basis of phenotypic diversity.

Methodology/Main Findings

Genome wide footprints of pig domestication and selection were identified using massive parallel sequencing of pooled reduced representation libraries (RRL) representing ∼2% of the genome from wild boar and four domestic pig breeds (Large White, Landrace, Duroc and Pietrain) which have been under strong selection for muscle development, growth, behavior and coat color. Using specifically developed statistical methods that account for DNA pooling, low mean sequencing depth, and sequencing errors, we provide genome-wide estimates of nucleotide diversity and genetic differentiation in pig. Widespread signals suggestive of positive and balancing selection were found and the strongest signals were observed in Pietrain, one of the breeds most intensively selected for muscle development. Most signals were population-specific but affected genomic regions which harbored genes for common biological categories including coat color, brain development, muscle development, growth, metabolism, olfaction and immunity. Genetic differentiation in regions harboring genes related to muscle development and growth was higher between breeds than between a given breed and the wild boar.

Conclusions/Significance

These results, suggest that although domesticated breeds have experienced similar selective pressures, selection has acted upon different genes. This might reflect the multiple domestication events of European breeds or could be the result of subsequent introgression of Asian alleles. Overall, it was estimated that approximately 7% of the porcine genome has been affected by selection events. This study illustrates that the massive parallel sequencing of genomic pools is a cost-effective approach to identify footprints of selection.     

Targeted massive parallel sequencing: the effective detection of novel causative mutations associated with hearing loss in small families

ABSTRACT: BACKGROUND: Hereditary hearing loss is one of the most common heterogeneous disorders, and genetic variants that can cause hearing loss have been identified in over fifty genes. Most of these hearing loss genes have been detected using classical genetic methods, typically starting with linkage analysis in large families with hereditary hearing loss. However, these classical strategies are not well suited for mutation analysis in smaller families who have insufficient genetic information. METHODS: Eighty known hearing loss genes were selected and simultaneously sequenced by targeted next-generation sequencing (NGS) in 8 Korean families with autosomal dominant non-syndromic sensorineural hearing loss. RESULTS: Five mutations in known hearing loss genes, including 1 nonsense and 4 missense mutations, were identified in 5 different genes (ACTG1, MYO1F, DIAPH1, POU4F3 and EYA4), and the genotypes for these mutations were consistent with the autosomal dominant inheritance pattern of hearing loss in each family. No mutational hot-spots were revealed in these Korean families. CONCLUSION: Targeted NGS allowed for the detection of pathogenic mutations in affected individuals who were not candidates for classical genetic studies. This report is the first documenting the effective use of an NGS technique to detect pathogenic mutations that underlie hearing loss in an East Asian population. Using this NGS technique to establish a database of common mutations in Korean patients with hearing loss and further data accumulation will contribute to the early diagnosis and fundamental therapies for hereditary hearing loss.     

Targeted massive parallel sequencing: the effective detection of novel causative mutations associated with hearing loss in small families

Background

Gaucher disease (GD) is due to deficiency of the glucocerebrosidase enzyme. It is panethnic, but its presentation reveals ethnicity-specific characteristics.

Methods

We evaluated the distribution, and clinical and genetic characteristics of GD patients in the Iberian Peninsula (IP). We analysed geographical distribution, demographic, genetic and clinical data, age at diagnosis, type, and years of therapy in 436 GD patients from the IP.

Results

The prevalence of GD was 1/149,000 inhabitants; 88.3% were type 1, 6.7% type 2, and 5.0% type 3. The mean age at diagnosis in type 1 was 28.7 years. A total of 72.7% were classified as having mild forms, 25.5% moderate, and 1.7% severe. Anemia and thrombocytopenia were present in 56% and 55%, respectively. Bone disease and hepatomegaly were reported in 62% and 68%, respectively, and were more likely in asplenic than in non-splenectomized patients. Sixty-nine mutant alleles were identified, and five mutations accounted for 75% of the GBA alleles. Several patients described in our series had interesting phenotypes. A total of 58.7% of patients had received enzyme replacement therapy and 12.6% were treated with miglustat.

Conclusions

A broad spectrum of GBA mutations is present in the IP, with 98.2% of type 1 GD being mild and 23.0% never treated. These data highlight genetic and phenotypic heterogeneities among geographic populations.     

RNA sequencing: new technologies and applications in cancer research

Acute myeloid leukemia (AML) is a fatal hematopoietic malignancy and has a prognosis that varies with its genetic complexity. However, there has been no appropriate integrative analysis on the hierarchy of different AML subtypes. Using Microwell-seq, a high-throughput single-cell mRNA sequencing platform, we analyzed the cellular hierarchy of bone marrow samples from 40 patients and 3 healthy donors. We also used single-cell single-molecule real-time (SMRT) sequencing to investigate the clonal heterogeneity of AML cells. From the integrative analysis of 191727 AML cells, we established a single-cell AML landscape and identified an AML progenitor cell cluster with novel AML markers. Patients with ribosomal protein high progenitor cells had a low remission rate. We deduced two types of AML with diverse clinical outcomes. We traced mitochondrial mutations in the AML landscape by combining Microwell-seq with SMRT sequencing. We propose the existence of a phenotypic “cancer attractor” that might help to define a common phenotype for AML progenitor cells. Finally, we explored the potential drug targets by making comparisons between the AML landscape and the Human Cell Landscape. We identified a key AML progenitor cell cluster. A high ribosomal protein gene level indicates the poor prognosis. We deduced two types of AML and explored the potential drug targets. Our results suggest the existence of a cancer attractor.     

基于NGS的染色质测序在肿瘤研究中的应用

染色质是真核生物细胞核内由核酸和蛋白质组成的复合结构,有着精密且复杂的三维结构。染色质除基本的DNA序列外,内部还存在着不同化学修饰,DNA-蛋白质相互作用,DNA-DNA相互作用和DNA-RNA相互作用,以上这些若发生改变都可能在肿瘤发生发展过程中起到至关重要的作用。通过不同的染色质测序方法,可以解析出这些改变,并进一步加深研究者对肿瘤形成机制的理解,最终应用于肿瘤的治疗。本文对常见的染色质测序技术部分原理和应用进行综述。     

Mutation screening in 86 known X-linked mental retardation genes by droplet-based multiplex PCR and massive parallel sequencing

Massive parallel sequencing has revolutionized the search for pathogenic variants in the human genome, but for routine diagnosis, re-sequencing of the complete human genome in a large cohort of patients is still far too expensive. Recently, novel genome partitioning methods have been developed that allow to target re-sequencing to specific genomic compartments, but practical experience with these methods is still limited. In this study, we have combined a novel droplet-based multiplex PCR method and next generation sequencing to screen patients with X-linked mental retardation (XLMR) for mutations in 86 previously identified XLMR genes. In total, affected males from 24 large XLMR families were analyzed, including three in whom the mutations were already known. Amplicons corresponding to functionally relevant regions of these genes were sequenced on an Illumina/Solexa Genome Analyzer II platform. Highly specific and uniform enrichment was achieved: on average, 67.9% unambiguously mapped reads were derived from amplicons, and for 88.5% of the targeted bases, the sequencing depth was sufficient to reliably detect variations. Potentially disease-causing sequence variants were identified in 10 out of 24 patients, including the three mutations that were already known, and all of these could be confirmed by Sanger sequencing. The robust performance of this approach demonstrates the general utility of droplet-based multiplex PCR for parallel mutation screening in hundreds of genes, which is a prerequisite for the diagnosis of mental retardation and other disorders that may be due to defects of a wide variety of genes. ELECTRONIC SUPPLEMENTARY MATERIAL: The online version of this article (doi:10.1007/s11568-010-9137-y) contains supplementary material, which is available to authorized users.