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Supplementary Figure 5 from Intrinsic Gene Expression Profiles of Gliomas Are a Better Predictor of Survival than Histology

Supplementary Figure 10 from Intrinsic Gene Expression Profiles of Gliomas Are a Better Predictor of Survival than Histology

Supplementary Figures 1-3 from Identification of Differentially Regulated Splice Variants and Novel Exons in Glial Brain Tumors Using Exon Expression Arrays

Supplementary Figure 6 from Intrinsic Gene Expression Profiles of Gliomas Are a Better Predictor of Survival than Histology

Supplementary Figure 3 from Intrinsic Gene Expression Profiles of Gliomas Are a Better Predictor of Survival than Histology

Gliomas are the most common primary brain tumors with heterogeneous morphology and variable prognosis. Treatment decisions in patients rely mainly on histologic classification and clinical parameters. However, differences between histologic subclasses and grades are subtle, and classifying gliomas is subject to a large interobserver variability. To improve current classification standards, we have performed gene expression profiling on a large cohort of glioma samples of all histologic subtypes and grades. We identified seven distinct molecular subgroups that correlate with survival. These include two favorable prognostic subgroups (median survival, >4.7 years), two with intermediate prognosis (median survival, 1–4 years), two with poor prognosis (median survival

Supplementary Figure 8 from Intrinsic Gene Expression Profiles of Gliomas Are a Better Predictor of Survival than Histology

Supplementary Figure 2 from Intrinsic Gene Expression Profiles of Gliomas Are a Better Predictor of Survival than Histology

Supplementary Figure 4 from Intrinsic Gene Expression Profiles of Gliomas Are a Better Predictor of Survival than Histology

Supplementary Table 3 from Identification of Differentially Regulated Splice Variants and Novel Exons in Glial Brain Tumors Using Exon Expression Arrays

Supplementary Tables 1-6 from Intrinsic Gene Expression Profiles of Gliomas Are a Better Predictor of Survival than Histology

Supplementary Figure 7 from Intrinsic Gene Expression Profiles of Gliomas Are a Better Predictor of Survival than Histology

Supplementary Table 1 from Identification of Differentially Regulated Splice Variants and Novel Exons in Glial Brain Tumors Using Exon Expression Arrays

Aberrant splice variants are involved in the initiation and/or progression of glial brain tumors. We therefore set out to identify splice variants that are differentially expressed between histologic subgroups of gliomas. Splice variants were identified using a novel platform that profiles the expression of virtually all known and predicted exons present in the human genome. Exon-level expression profiling was done on 26 glioblastomas, 22 oligodendrogliomas, and 6 control brain samples. Our results show that Human Exon arrays can identify subgroups of gliomas based on their histologic appearance and genetic aberrations. We next used our expression data to identify differentially expressed splice variants. In two independent approaches, we identified 49 and up to 459 exons that are differentially spliced between glioblastomas and oligodendrogliomas, a subset of which (47% and 33%) were confirmed by reverse transcription-PCR (RT-PCR). In addition, exon level expression profiling also identified >700 novel exons. Expression of ∼67% of these candidate novel exons was confirmed by RT-PCR. Our results indicate that exon level expression profiling can be used to molecularly classify brain tumor subgroups, can identify differentially regulated splice variants, and can identify novel exons. The splice variants identified by exon level expression profiling may help to detect the genetic changes that cause or maintain gliomas and may serve as novel treatment targets. [Cancer Res 2007;67(12):5635–8]

Supplementary Figure 9 from Intrinsic Gene Expression Profiles of Gliomas Are a Better Predictor of Survival than Histology

Supplementary Table 2 from Identification of Differentially Regulated Splice Variants and Novel Exons in Glial Brain Tumors Using Exon Expression Arrays

Supplementary Methods from Intrinsic Gene Expression Profiles of Gliomas Are a Better Predictor of Survival than Histology

Foraminifera are a species-rich phylum of rhizarian protists that are highly abundant in many marine environments and play a major role in global carbon cycling. Species recognition in Foraminifera is mainly based on morphological characters and nuclear 18S ribosomal RNA barcoding. The 18S rRNA contains variable sequence regions that allow for the identification of most foraminiferal species. Still, some species show limited variability, while others contain high levels of intragenomic polymorphisms, thereby complicating species identification. The use of additional, easily obtainable molecular markers other than 18S rRNA will enable more detailed investigation of evolutionary history, population genetics and speciation in Foraminifera. Here we present the first mitochondrial cytochrome c oxidase subunit 1 (COI) gene sequences (“barcodes”) of Foraminifera. We applied shotgun sequencing to single foraminiferal specimens, assembled COI, and developed primers that allow amplification of COI in a wide range of foraminiferal species. We obtained COI sequences of 49 specimens from 17 species from the orders Rotaliida and Miliolida. Phylogenetic analysis showed that the COI tree is largely congruent with previously published 18S rRNA phylogenies. Furthermore, species delimitation with ASAP and ABGD algorithms showed that foraminiferal species can be identified based on COI barcodes.