Pathway and gene-set activation measurement from mRNA expression data: the tissue distribution of human pathways

BACKGROUND: Interpretation of lists of genes or proteins with altered expression is a critical and time-consuming part of microarray and proteomics research, but relatively little attention has been paid to methods for extracting biological meaning from these output lists. One powerful approach is to examine the expression of predefined biological pathways and gene sets, such as metabolic and signaling pathways and macromolecular complexes. Although many methods for measuring pathway expression have been proposed, a systematic analysis of the performance of multiple methods over multiple independent data sets has not previously been reported. RESULTS: Five different measures of pathway expression were compared in an analysis of nine publicly available mRNA expression data sets. The relative sensitivity of the metrics varied greatly across data sets, and the biological pathways identified for each data set are also dependent on the choice of pathway activation metric. In addition, we show that removing incoherent pathways prior to analysis improves specificity. Finally, we create and analyze a public map of pathway expression in human tissues by gene-set analysis of a large compendium of human expression data. CONCLUSION: We show that both the detection sensitivity and identity of pathways significantly perturbed in a microarray experiment are highly dependent on the analysis methods used and how incoherent pathways are treated. Analysts should thus consider using multiple approaches to test the robustness of their biological interpretations. We also provide a comprehensive picture of the tissue distribution of human gene pathways and a useful public archive of human pathway expression data

Digital Genome-Wide ncRNA Expression, Including SnoRNAs, across 11 Human Tissues Using PolyA-Neutral Amplification

Non-coding RNAs (ncRNAs) are an essential class of molecular species that have been difficult to monitor on high throughput platforms due to frequent lack of polyadenylation. Using a polyadenylation-neutral amplification protocol and next-generation sequencing, we explore ncRNA expression in eleven human tissues. ncRNAs 7SL, U2, 7SK, and HBII-52 are expressed at levels far exceeding mRNAs. C/D and H/ACA box snoRNAs are associated with rRNA methylation and pseudouridylation, respectively: spleen expresses both, hypothalamus expresses mainly C/D box snoRNAs, and testes show enriched expression of both H/ACA box snoRNAs and RNA telomerase TERC. Within the snoRNA 14q cluster, 14q(I-6) is expressed at much higher levels than other cluster members. More reads align to mitochondrial than nuclear tRNAs. Many lincRNAs are actively transcribed, particularly those overlapping known ncRNAs. Within the Prader-Willi syndrome loci, the snoRNA HBII-85 (group I) cluster is highly expressed in hypothalamus, greater than in other tissues and greater than group II or III. Additionally, within the disease locus we find novel transcription across a 400,000 nt span in ovaries. This genome-wide polyA-neutral expression compendium demonstrates the richness of ncRNA expression, their high expression patterns, their function-specific expression patterns, and is publicly available

MR angiography of large-vessel intracranial stenosis after cryptococcal meningitis

AbstractWe present a case of cryptococcal meningoencephalitis producing narrowing of both middle cerebral arteries on MRI/MRA, described in a 56-year-old man with a history of Wegener’s granulomatosis. Diagnosis was based on the presence of cryptococcal antigen in serum and CSF. Imaging performed seven months after initial presentation demonstrated thickened enhancing leptomeninges with focal inflammatory masses in the Sylvian fissures. To our knowledge, this striking appearance has not been previously demonstrated simultaneously on MRI/MRA

Medical Image Computing and Computer-Assisted Intervention: Editorial: Medical Image Computing and Computer-Assisted Intervention: Editorial

During the past years, the International Conference on Medical Image Computing and Computer Assisted Intervention (MICCAI) has become a premier international conference with in-depth papers on the multidisciplinary fields of medical image processing, computer-assisted intervention and medical robotics. The conference brings together clinicians, biological scientists, computer scientists, engineers, physicists and other researchers and offers them a forum to exchange ideas in these exciting and rapidly growing fields. The seventh edition of MICCAI was held in Saint-Malo, Brittany, France from 26 to 29 September 2004, the first time in France since the conference was formed in 1998. One objective of the 2004 edition was to encourage contributions strengthening the links between clinical applications and innovative biomedical science and engineering research, with a special emphasis on validation issues. The impact of MICCAI increases each year and the quality and quantity of submitted papers was very remarkable. The conference attracted an overall attendance of 607 participants from more than 30 different countries. The program committee received 516 full submissions (8 pages in length) and 101 short communications (2 pages) from 36 different countries and 6 continents; a figure below shows the distribution of Miccai 2004 submissions per world regions. All submissions were reviewed by up to 4 external reviewers from the Scientific Review Committee and a primary reviewer from the Program Committee. All reviews were then considered by the MICCAI 2004 Program Committee, resulting in the acceptance of 235 full papers and 33 short communications. The normal mode of presentation at MICCAI 2004 was as a poster; in addition, 46 papers were chosen for oral presentation. All of the full papers accepted were included in the proceedings1 in 8-page format. All of the accepted 2-page short communications were also included, and appeared at the meeting as posters. A figure below shows the distribution of accepted contributions by topic, topics being defined from the primary keyword of the submission. Of all papers presented, 14 were selected for possible inclusion in this special issue of Medical Image Analysis on MICCAI 2004. The selection was based on the suitability of the subject matter for MedIA, original comments, MICCAI awards and scores from the conference peer review, along with the quality of the presentations and posters during the meeting. The authors were asked to expand their conference articles in order to reach the level of an archived journal publication in quality and detail. After all, 7 of the 14 invited papers made their way through the regular review process of Medical Image Analysis and could be included in this issue, two of them having received a specific award during the conference (Perperidis et al. and Valtorta and Mazza papers). One of the most important trends in current research in medical image analysis follow the evolution of the biomedical imaging community by incorporating new spatio-temporal dimensions to the anatomical and functional data acquired and used, and by matching these observations with physically based models. This leads to better understand how normal and pathological organs behaves and also participates to integrate new effectors and image data during computer assisted interventional procedures (surgery, interventional radiology, ...). The seven papers we selected for this issue reflect this trend, with particular emphasis on analysis of time varying images with data fusion and incorporation of physically based models (Perperidis et al., Sermesant et al.), segmenting highly difficult images of normal and pathological organs (Jackowski et al. on high dimensional DT-MRI data and Prastawa et al. on low SNR neonatal brain MRI), or jointly modelling and segmenting anatomical structures (Tsai et al.), building realistic physically based models (Valtorta and Mazza) and finally integrating intra-operative imaging modalities during computer assisted interventional procedures (Burschka et al.). Finally, we would like to thank all the reviewers who devoted their time and effort to help us. Their contribution, together with the significant support from the publishing office, the MICCAI program board, and the MICCAI organizing board made this special issue possible within the time limits of the tight editorial schedule