An introduction to the informatics of "next-generation" sequencing

The regulatory network for petal anthocyanin pigmentation is shaped by the MYB5a/NEGAN transcription factor in Mimulus

Much of the visual diversity of angiosperms is due to the frequent evolution of novel pigmentation patterns in flowers. The gene network responsible for anthocyanin pigmentation, in particular, has become a model for investigating how genetic changes give rise to phenotypic innovation. In the monkeyflower genus Mimulus, an evolutionarily recent gain of petal lobe anthocyanin pigmentation in M. luteus var. variegatus was previously mapped to genomic region pla2. Here, we use sequence and expression analysis, followed by transgenic manipulation of gene expression, to identify MYB5a—orthologous to the NEGAN transcriptional activator from M. lewisii—as the gene responsible for the transition to anthocyanin-pigmented petals in M. l. variegatus. In other monkeyflower taxa, MYB5a/NEGAN is part of a reaction-diffusion network that produces semi-repeating spotting patterns, such as the array of spots in the nectar guides of both M. lewisii and M. guttatus. Its co-option for the evolution of an apparently non-patterned trait—the solid petal lobe pigmentation of M. l. variegatus—illustrates how reaction-diffusion can contribute to evolutionary novelty in non-obvious ways. Transcriptome sequencing of a MYB5a RNAi line of M. l. variegatus reveals that this genetically simple change, which we hypothesize to be a regulatory mutation in cis to MYB5a, has cascading effects on gene expression, not only on the enzyme-encoding genes traditionally thought of as the targets of MYB5a but also on all of its known partners in the anthocyanin regulatory network.

Management of familial cancer: sequencing, surveillance and society

The clinical management of familial cancer begins with recognition of patterns of cancer occurrence suggestive of genetic susceptibility in a proband or pedigree, to enable subsequent investigation of the underlying DNA mutations. In this regard, next-generation sequencing of DNA continues to transform cancer diagnostics, by enabling screening for cancer-susceptibility genes in the context of known and emerging familial cancer syndromes. Increasingly, not only are candidate cancer genes sequenced, but also entire 'healthy' genomes are mapped in children with cancer and their family members. Although large-scale genomic analysis is considered intrinsic to the success of cancer research and discovery, a number of accompanying ethical and technical issues must be addressed before this approach can be adopted widely in personalized therapy. In this Perspectives article, we describe our views on how the emergence of new sequencing technologies and cancer surveillance strategies is altering the framework for the clinical management of hereditary cancer. Genetic counselling and disclosure issues are discussed, and strategies for approaching ethical dilemmas are proposed.

Assessment of the impact of using a reference transcriptome in mapping short RNA-Seq reads

RNA-Seq has become increasingly popular in transcriptome profiling. The major challenge in RNA-Seq data analysis is the accurate mapping of junction reads to their genomic origins. To detect splicing sites in short reads, many RNA-Seq aligners use reference transcriptome to inform placement of junction reads. However, no systematic evaluation has been performed to assess or quantify the benefits of incorporating reference transcriptome in mapping RNA-Seq reads. In this paper, we have studied the impact of reference transcriptome on mapping RNA-Seq reads, especially on junction ones. The same dataset were analysed with and without RefGene transcriptome, respectively. Then a Perl script was developed to analyse and compare the mapping results. It was found that about 50–55% junction reads can be mapped to the same genomic regions regardless of the usage of RefGene model. More than one-third of reads fail to be mapped without the help of a reference transcriptome. For “Alternatively” mapped reads, i.e., those reads mapped differently with and without RefGene model, the mappings without RefGene model are usually worse than their corresponding alignments with RefGene model. For junction reads that span more than two exons, it is less likely to align them correctly without the assistance of reference transcriptome. As the sequencing technology evolves, the read length is becoming longer and longer. When reads become longer, they are more likely to span multiple exons, and thus the mapping of long junction reads is actually becoming more and more challenging without the assistance of reference transcriptome. Therefore, the advantages of using reference transcriptome in the mapping demonstrated in this study are becoming more evident for longer reads. In addition, the effect of the completeness of reference transcriptome on mapping of RNA-Seq reads is discussed.

A novel approach for transcription factor analysis using SELEX with high-throughput sequencing (TFAST)

Background

In previous work, we designed a modified aptamer-free SELEX-seq protocol (afSELEX-seq) for the discovery of transcription factor binding sites. Here, we present original software, TFAST, designed to analyze afSELEX-seq data, validated against our previously generated afSELEX-seq dataset and a model dataset. TFAST is designed with a simple graphical interface (Java) so that it can be installed and executed without extensive expertise in bioinformatics. TFAST completes analysis within minutes on most personal computers.

Methodology

Once afSELEX-seq data are aligned to a target genome, TFAST identifies peaks and, uniquely, compares peak characteristics between cycles. TFAST generates a hierarchical report of graded peaks, their associated genomic sequences, binding site length predictions, and dummy sequences.

Principal Findings

Including additional cycles of afSELEX-seq improved TFAST's ability to selectively identify peaks, leading to 7,274, 4,255, and 2,628 peaks identified in two-, three-, and four-cycle afSELEX-seq. Inter-round analysis by TFAST identified 457 peaks as the strongest candidates for true binding sites. Separating peaks by TFAST into classes of worst, second-best and best candidate peaks revealed a trend of increasing significance (e-values 4.5×10¹², 2.9×10⁻⁴⁶, and 1.2×10⁻⁷³) and informational content (11.0, 11.9, and 12.5 bits over 15 bp) of discovered motifs within each respective class. TFAST also predicted a binding site length (28 bp) consistent with non-computational experimentally derived results for the transcription factor PapX (22 to 29 bp).

Conclusions/Significance

TFAST offers a novel and intuitive approach for determining DNA binding sites of proteins subjected to afSELEX-seq. Here, we demonstrate that TFAST, using afSELEX-seq data, rapidly and accurately predicted sequence length and motif for a putative transcription factor's binding site.