Autosomal-dominant spinocerebellar ataxias constitute a large, heterogeneous group of progressive neurodegenerative diseases with multiple types. To date, classical genetic studies have revealed 31 distinct genetic forms of spinocerebellar ataxias and identified 19 causative genes. Traditional positional cloning strategies, however, have limitations for finding causative genes of rare Mendelian disorders. Here, we used a combined strategy of exome sequencing and linkage analysis to identify a novel spinocerebellar ataxia causative gene, TGM6. We sequenced the whole exome of four patients in a Chinese four-generation spinocerebellar ataxia family and identified a missense mutation, c.1550T–G transition (L517W), in exon 10 of TGM6. This change is at a highly conserved position, is predicted to have a functional impact, and completely cosegregated with the phenotype. The exome results were validated using linkage analysis. The mutation we identified using exome sequencing was located in the same region (20p13–12.2) as that identified by linkage analysis, which cross-validated TGM6 as the causative spinocerebellar ataxia gene in this family. We also showed that the causative gene could be mapped by a combined method of linkage analysis and sequencing of one sample from the family. We further confirmed our finding by identifying another missense mutation c.980A–G transition (D327G) in exon seven of TGM6 in an additional spinocerebellar ataxia family, which also cosegregated with the phenotype. Both mutations were absent in 500 normal unaffected individuals of matched geographical ancestry. The finding of TGM6 as a novel causative gene of spinocerebellar ataxia illustrates whole-exome sequencing of affected individuals from one family as an effective and cost efficient method for mapping genes of rare Mendelian disorders and the use of linkage analysis and exome sequencing for further improving efficiency.

Background: Knowledge of the structure of gene expression is essential for mammalian transcriptomics research. We analyzed a collection of more than one million porcine expressed sequence tags (ESTs), of which two-thirds were generated in the Sino-Danish Pig Genome Project and one-third are from public databases. The Sino-Danish ESTs were generated from one normalized and 97 non-normalized cDNA libraries representing 35 different tissues and three developmental stages. Results: Using the Distiller package, the ESTs were assembled to roughly 48,000 contigs and 73,000 singletons, of which approximately 25% have a high confidence match to UniProt. Approximately 6,000 new porcine gene clusters were identified. Expression analysis based on the non-normalized libraries resulted in the following findings. The distribution of cluster sizes is scaling invariant. Brain and testes are among the tissues with the greatest number of different expressed genes, whereas tissues with more specialized function, such as developing liver, have fewer expressed genes. There are at least 65 high confidence housekeeping gene candidates and 876 cDNA library-specific gene candidates. We identified differential expression of genes between different tissues, in particular brain/spinal cord, and found patterns of correlation between genes that share expression in pairs of libraries. Finally, there was remarkable agreement in expression between specialized tissues according to Gene Ontology categories. Conclusion: This EST collection, the largest to date in pig, represents an essential resource for annotation, comparative genomics, assembly of the pig genome sequence, and further porcine transcription studies.

Background: Gene conversion causes a non-reciprocal transfer of genetic information between similar sequences. Gene conversion can both homogenize genes and recruit point mutations thereby shaping the evolution of multigene families. In the rice genome, the large number of duplicated genes increases opportunities for gene conversion.Results: To characterize gene conversion in rice, we have defined 626 multigene families in which 377 gene conversions were detected using the GENECONV program. Over 60% of the conversions we detected were between chromosomes. We found that the inter-chromosomal conversions distributed between chromosome 1 and 5, 2 and 6, and 3 and 5 are more frequent than genome average (Z-test, P < 0.05). The frequencies of gene conversion on the same chromosome decreased with the physical distance between gene conversion partners. Ka/Ks analysis indicates that gene conversion is not tightly linked to natural selection in the rice genome. To assess the contribution of segmental duplication on gene conversion statistics, we determined locations of conversion partners with respect to inter-chromosomal segment duplication. The number of conversions associated with segmentation is less than ten percent. Pseudogenes in the rice genome with low similarity toArabidopsisgenes showed greater likelihood for gene conversion than those with high similarity toArabidopsisgenes. Functional annotations suggest that at least 14 multigene families related to disease or bacteria resistance were involved in conversion events.Conclusion: The evolution of gene families in the rice genome may have been accelerated by conversion with pseudogenes. Our analysis suggests a possible role for gene conversion in the evolution of pathogen-response genes.

We describe an algorithm, ReAS, to recover ancestral sequences for transposable elements (TEs) from the unassembled reads of a whole genome shotgun. The main assumptions are that these TEs must exist at high copy numbers across the genome and must not be so old that they are no longer recognizable in comparison to their ancestral sequences. Tested on the japonica rice genome, ReAS was able to reconstruct all of the high copy sequences in the Repbase repository of known TEs, and increase the effectiveness of RepeatMasker in identifying TEs from genome sequences. Transposable elements (TEs) are a major component of the genomes of multicellular organisms. They are parasitic creatures that invade the genome, insert multiple copies of themselves, and then die. All we see now are the decayed remnants of their ancestral sequences. Reconstruction of these ancestral sequences can bring dead TEs back to life. Algorithms for detecting TEs compare present-day sequences to a library of ancestral sequences. Unknown to many, pervasive use of whole genome shotgun (WGS) methods in large-scale sequencing have made TE reconstructions increasingly problematic. To minimize assembly errors, WGS methods must reject the highly repetitive sequences that characterize most TEs, especially the most recent TEs, which are the least diverged from their ancestral sequences (and most informative for reconstruction). This is acceptable to many, because the most important parts of the genes are not repetitive, but for the TE aficionados, it is a problem. ReAS is a novel algorithm that does TE reconstruction using only the unassembled reads of a WGS. Tested against the WGS for japonica rice, it is shown to produce a library that is superior to the manually curated Repbase database of known ancestral TEs.