In PLoS Computational Biology this week, a trio of investigators at Stanford University presents a computational approach "to discover patterns of biological progression underlying microarray gene expression data," called Sample Progression Discovery, or SPD. When applied to a mouse embryonic stem cell differentiation data set, "SPD uncovered a landscape of ESC differentiation into various lineages and genes that represent both generic and lineage specific processes," the team writes. The researchers also report their use of this SPD approach on prostate cancer and normal B-cell differentiation data sets. Overall, the team writes, SPD "provides a likely biological progression underlying a microarray dataset and, perhaps more importantly, the candidate genes that regulate that progression."
Over in PLoS Genetics, an international team led by researchers at Children's Hospital Boston shows that "loss-of-function mutations in PTPN11 cause metachondromatosis, but not Ollier disease or Maffucci syndrome,” all of which are pediatric tumor syndromes. First, the team performed linkage analysis with high-density SNP arrays in a single family affected by metachondromatosis. Then, it used targeted arrays to capture exons and promoter sequences from the linked interval in 16 members of 11 metachondromatosis families. From that, they "identified heterozygous putative loss-of-function mutations in PTPN11 in 4 of the 11," the researchers write. Finally, by Sanger sequencing PTPN11 coding regions in 17 families and samples from 54 patients with either Ollier disease or Maffucci syndrome, the researchers distinguished heterozygous loss-of-function mutations in PTPN11 as a frequent cause of metachondromatosis," though not the related conditions.
Researchers at the Medical University of Vienna in Austria discuss how mutations in four muscular dystrophy genes are associated with DNA damage and genomic instability, somatic aneuploidy, and malignant sarcoma susceptibility in PLoS Genetics this week. "These novel aspects of molecular pathologies common to muscular dystrophies and tumor biology will potentially influence the strategies to combat these diseases," the authors write.
And in PLoS One, researchers at the Norwegian University of Science and Technology report their use of published ChIP-seq data and a peak caller "to create comprehensive benchmark data sets for prediction methods which use known descriptors or binding motifs to detect TFBS [transcription factor binding sites] in genomic sequences." Overall, the team writes, its ChIP-seq benchmark "shows that sequence conservation mainly improves detection of strong" TFBS.