SciCombinator

Malignant mesothelioma (MM) has distinct histological subtypes (epithelioid, sarcomatoid and biphasic) with variable behaviour and prognoses. It is well recognised that survival time varies with the histological subtype of MM. It is not known, however, if asbestos exposure characteristics (type of asbestos, degree of exposure) are associated with different histological subtypes.

Technological advances enable the cost-effective acquisition of Multi-Modal Data Sets (MMDS) composed of measurements for multiple, high-dimensional data types obtained from a common set of bio-samples. The joint analysis of the data matrices associated with the different data types of a MMDS should provide a more focused view of the biology underlying complex diseases such as cancer that would not be apparent from the analysis of a single data type alone. As multi-modal data rapidly accumulate in research laboratories and public databases such as The Cancer Genome Atlas (TCGA), the translation of such data into clinically actionable knowledge has been slowed by the lack of computational tools capable of analyzing MMDSs. Here, we describe the Joint Analysis of Many Matrices by ITeration (JAMMIT) algorithm that jointly analyzes the data matrices of a MMDS using sparse matrix approximations of rank-1.

Parkinson’s disease (PD) presents clinically with several motor subtypes that exhibit variable treatment response and prognosis. Here, we investigated genetic variants for their potential association with PD motor phenotype and progression.

BACKGROUND: Multigenic diseases are often associated with protein complexes or interactions involved in the same pathway. We wanted to estimate to what extent this is true given a consolidated protein interaction data set. The study stresses data integration and data representation issues. RESULTS: We constructed 497 multigenic disease groups from OMIM and tested for overlaps with interaction and pathway data. A total of 159 disease groups had significant overlaps with protein interaction data consolidated by iRefIndex. A further 68 disease overlaps were found only in the KEGG pathway database. No single database contained all significant overlaps thus stressing the importance of data integration. We also found that disease groups overlapped with all three interaction data types: n-ary, spoke-represented complexes and binary data – thus stressing the importance of considering each of these data types separately. CONCLUSIONS: Almost half of our multigenic disease groups could potentially be explained by protein complexes and pathways. However, the fact that no database or data type was able to cover all disease groups suggests that no single database has systematically covered all disease groups for potential related complex and pathway data. This survey provides a basis for further curation efforts to confirm and search for overlaps between diseases and interaction data. The accompanying R script can be used to reproduce the work and track progress in this area as databases change. Disease group overlaps can be further explored using the iRefscape plugin for Cytoscape.

Recent technologies have made it cost-effective to collect diverse types of genome-wide data. Computational methods are needed to combine these data to create a comprehensive view of a given disease or a biological process. Similarity network fusion (SNF) solves this problem by constructing networks of samples (e.g., patients) for each available data type and then efficiently fusing these into one network that represents the full spectrum of underlying data. For example, to create a comprehensive view of a disease given a cohort of patients, SNF computes and fuses patient similarity networks obtained from each of their data types separately, taking advantage of the complementarity in the data. We used SNF to combine mRNA expression, DNA methylation and microRNA (miRNA) expression data for five cancer data sets. SNF substantially outperforms single data type analysis and established integrative approaches when identifying cancer subtypes and is effective for predicting survival.

The currently recognised subtypes of anal canal/perianal adenocarcinoma are those arising from low rectal mucosa or columnar cuff, fistula-related tumours and anal gland carcinoma. This following report presents two examples of a hitherto undescribed subtype of perianal adenocarcinoma with an intestinal phenotype.

Starting with conformations of calcium-binding sites in parvalbumin and integrin (representative structures of EF-hand and calcium blade zones, respectively) we introduce four new different local Ca(2+)-recognition units in proteins: a one-residue unit type I (ORI); a three-residue unit type I (TRI); a one-residue unit type II (ORII) and a three-residue unit type II (TRII). Based on the amount and nature of variable atoms, the type I and II units theoretically can have four and twelve variants, respectively. Analysis of known “Ca(2+)-bound functional niches” in proteins revealed presence of almost all possible variants of Ca(2+)-recognition units in actual structures. Parvalbumin, integrin alpha-IIb and sixteen other proteins with different Ca(2+)-bound functional niches contain various consecutively joined combinations of OR(I/II) and TR(I/II) units. Such a OR(I/II)+TR(I/II) joint unit forms a tripeptide, which uses three main-chain atoms for metal binding: nitrogenn (Donor), oxygenn (Acceptor) and nitrogenn+2 (Donor). Thus, taken together, the described ORI, TRI, ORII and TRII units can serve as elementary blocks to construct more complex calcium recognizing substructures in a variety of calcium binding sites of unrelated proteins.

Angiomatous meningioma (AM) is a rare subtype of meningioma characterized by highly vascular tumor tissue comprised predominantly of variable sized hyalinized blood vessels. The aim is to evaluate the clinical, radiological features of AM and the long term prognosis in a single neurosurgical center.

Heterozygous mutations in COL10A1 underlie metaphyseal chondrodysplasia, Schmid type (MCDS), an autosomal dominant skeletal dysplasia.

We propose an addition to the Snosek et al. classification to include a subtype variant of sternalis muscle: mixed type and triple subtype.