|1.||Current genetic methodologies in the identification of disaster victims and in forensic analysis.|
|Ziętkiewicz E, Witt M, Daca P, Zebracka-Gala J, Goniewicz M, Jarząb B, Witt M.|
|J Appl Genet. 2011 Oct 15. [Epub ahead of print]|
|PMID: 22002120 [PubMed - as supplied by publisher]|
|2.||Rapid detection of gene mutations responsible for non-syndromic aortic aneurysm and dissection using two different methods: resequencing microarray technology and next-generation sequencing.|
|Sakai H, Suzuki S, Mizuguchi T, Imoto K, Yamashita Y, Doi H, Kikuchi M, Tsurusaki Y, Saitsu H, Miyake N, Masuda M, Matsumoto N.|
|Hum Genet. 2011 Oct 15. [Epub ahead of print]|
|PMID: 22001912 [PubMed - as supplied by publisher]|
Aortic aneurysm and/or dissection (AAD) is a life-threatening condition, and several syndromes are known to be related to AAD. In this study, two new technologies, resequencing array technology (ResAT) and next-generation sequencing (NGS), were used to analyze eight genes associated with syndromic AAD in 70 patients with non-syndromic AAD. Eighteen sequence variants were detected using both ResAT and NGS. In addition one of these sequence variants was detected by ResAT only and two additional variants by NGS only. Three of the 18 variants are likely to be pathogenic (in 4.3% of AAD patients and in 8.6% of a subset of patients with thoracic AAD), highlighting the importance of genetic analysis in non-syndromic AAD. ResAT and NGS similarly detected most, but not all, of the variants. Resequencing array technology was a rapid and efficient method for detecting most nucleotide substitutions, but was unable to detect short insertions/deletions, and it is impractical to update custom arrays frequently. Next-generation sequencing was able to detect almost all types of mutation, but requires improved informatics methods.
|3.||The genomics of autoimmune disease in the era of genome-wide association studies and beyond.|
|Lessard CJ, Ice JA, Adrianto I, Wiley G, Kelly JA, Gaffney PM, Montgomery CG, Moser KL.|
|Autoimmun Rev. 2011 Oct 7. [Epub ahead of print]|
|PMID: 22001415 [PubMed - as supplied by publisher]|
|4.||Sequencing of BAC pools by different next generation sequencing platforms and strategies.|
|Taudien S, Steuernagel B, Ariyadasa R, Schulte D, Schmutzer T, Groth M, Felder M, Petzold A, Scholz U, Mayer KF, Stein N, Platzer M.|
|BMC Res Notes. 2011 Oct 14;4(1):411. [Epub ahead of print]|
|PMID: 21999860 [PubMed - as supplied by publisher]|
Next generation sequencing of BACs is a viable option for deciphering the sequence of even large and highly repetitive genomes. In order to optimize this strategy, we examined the influence of read length on the quality of Roche/454 sequence assemblies, to what extent Illumina/Solexa mate pairs (MPs) improve the assemblies by scaffolding and whether barcoding of BACs is dispensable.
Sequencing four BACs with both FLX and Titanium technologies revealed similar sequencing accuracy, but showed that the longer Titanium reads produce considerably less misassemblies and gaps. The 454 assemblies of 96 barcoded BACs were improved by scaffolding 79% of the total contig length with MPs from a non-barcoded library. Assembly of the unmasked 454 sequences without separation by barcodes revealed chimeric contig formation to be a major problem, encompassing 47% of the total contig length. Masking the sequences reduced this fraction to 24%.
Optimal BAC pool sequencing should be based on the longest available reads, with barcoding essential for a comprehensive assessment of both repetitive and non-repetitive sequence information. When interest is restricted to non-repetitive regions and repeats are masked prior to assembly, barcoding is non-essential. In any case, the assemblies can be improved considerably by scaffolding with non-barcoded BAC pool MPs.
|5.||Chipster: user-friendly analysis software for microarray and other high-throughput data.|
|Kallio MA, Tuimala JT, Hupponen T, Klemela P, Gentile M, Scheinin I, Koski M, Kaki J, Korpelainen EI.|
|BMC Genomics. 2011 Oct 14;12(1):507. [Epub ahead of print]|
|PMID: 21999641 [PubMed - as supplied by publisher]|
The growth of high-throughput technologies such as microarrays and next generation sequencing has been accompanied by active research in data analysis methodology, producing new analysis methods at a rapid pace. While most of the newly developed methods are freely available, their use requires substantial computational skills. In order to enable non-programming biologists to benefit from the method development in a timely manner, we have created the Chipster software.
Chipster (http://chipster.csc.fi/) brings a powerful collection of data analysis methods within the reach of bioscientists via its intuitive graphical user interface. Users can analyze and integrate different data types such as gene expression, miRNA and aCGH. The analysis functionality is complemented with rich interactive visualizations, allowing users to select datapoints and create new gene lists based on these selections. Importantly, users can save the performed analysis steps as reusable, automatic workflows, which can also be shared with other users. Being a versatile and easily extendable platform, Chipster can be used for microarray, proteomics and sequencing data. In this article we describe its comprehensive collection of analysis and visualization tools for microarray data using three case studies.
Chipster is a user-friendly analysis software for high-throughput data. Its intuitive graphical user interface enables biologists to access a powerful collection of data analysis and integration tools, and to visualize data interactively. Users can collaborate by sharing analysis sessions and workflows. Chipster is open source, and the server installation package is freely available.