Understanding the hidden story within large amounts of high throughput data along with the ever-evolving genomic resources can be very daunting. The LAGA has a committed team of bioinformaticians with extensive experience in microarray and NGS technology, informatics and molecular biology - our goal is to apply the necessary tools to understand the underlying biological message.
LAGA offers acquisition, processing, statistical analysis, integration, and visualization of high-density genomic and transcriptome information from simple to complex experimental designs using one of several platforms. LAGA offers services on primary data and downstream functional analyses. The list of published studies provided at the end of this page demonstrates the expertise of our highly qualified personnel.
Your research projects will benefit from our experience in the following areas:
• gene expression profiling 12,13, prediction of alternative transcripts 8, copy number variation 1,2,14,3,5,6,7,15 from any microarray platform or from next-generation sequencing (NGS)
• methylation profiling from any microarray platform
• SNP genotyping from Illumina beadchips
• exon-level analysis of multiple samples profiled both by microarray and RNA-Seq for a truly comprehensive view of the transcriptome, including mutation expression levels and global changes in splicing patterns
• mapping next generation sequencing reads to reference sequences10, parsing and analysis of results for genome wide structural variations 9,11, including copy number profiles, detection of breakpoints, detection of mutations, detection of fusion transcripts, alternative splicing profiles, visualization of the whole genome/transcriptome datasets 4,16,17 using Nexus Copy Number, UCSC browser, development of experimental assays to validate and score the sequence data analysis results
• cross technology and cross data type (e.g. breast cancer and prostate cancer) comparative analysis18, use of internal or publically available databases containing expression microarray, aCGH and NGS data
• correlation of this information to biological endpoints 2,5,18
• various biostatistical analyses on microarray data sets (power calculation, logistic regression modeling, ROC analysis, Kaplan Meier estimator, ANOVA on slopes from linear regression analysis, Cox proportional hazards model, principal component analysis)
• various visualizations of data sets and analytical results
• integrating clinical information with expression/genomic data
• finding and visualizing pathways for differentially expressed genes
• clustering analyses (all commonly used methods)
• custom-tailored analyses for individual questions
• incorporation of annotations, functional annotations and functional group analysis via gene ontology (GO) information
• statistical expertise in discussion of results
• development of novel analysis tools as required by the customer’s project
• data publishing to public microarray data repositories such as NCBI’s Gene Expression Omnibus (GEO) in MIAME compliant form
Software includes:
• GeneSpring from Agilent Technologies
• Nexus Copy Number from BioDiscovery
• CGH Analytics from Agilent Technologies
• Acuity from Molecular Devices
• R and BioConductor
• GenomeStudio from Illumina
• Ingenuity Pathways Analysis
• SignalMap and NimbleScan from Roche NimbleGen
References:
1. Paris, P.L. et al. High-resolution analysis of paraffin-embedded and formalin-fixed prostate tumors using comparative genomic hybridization to genomic microarrays. Am J Pathol 162, 763-70 (2003).
2. Paris, P.L. et al. Whole genome scanning identifies genotypes associated with recurrence and metastasis in prostate tumors. Hum Mol Genet 13, 1303-13 (2004).
3. Paris, P.L. et al. Preliminary evaluation of prostate cancer metastatic risk biomarkers. Int J Biol Markers 20, 141-5 (2005).
4. Raphael, B.J. et al. A sequence-based survey of the complex structural organization of tumor genomes. Genome Biol 9, R59 (2008).
5. Paris, P.L. et al. A group of genome-based biomarkers that add to a Kattan nomogram for predicting progression in men with high-risk prostate cancer. Clin Cancer Res 16, 195-202 (2010).
6. Paris, P.L. et al. Genomic profiling of hormone-naive lymph node metastases in patients with prostate cancer. Neoplasia 8, 1083-9 (2006).
7. Paris, P.L. et al. High resolution oligonucleotide CGH using DNA from archived prostate tissue. Prostate 67, 1447-55 (2007).
8. Lapuk, A. et al. Exon-level microarray analyses identify alternative splicing programs in breast cancer. Mol Cancer Res 8, 961-74 (2010).
9. Yu, M. et al. Superficial, nodular, and morpheiform basal-cell carcinomas exhibit distinct gene expression profiles. J Invest Dermatol 128, 1797-805 (2008).
10. Yu, M. et al. Lichen planopilaris and pseudopelade of Brocq involve distinct disease associated gene expression patterns by microarray. J Dermatol Sci 57, 27-36 (2010).
11. van Duin, M. et al. Construction and application of a full-coverage, high-resolution, human chromosome 8q genomic microarray for comparative genomic hybridization. Cytometry A 63, 10-9 (2005).
12. Paris, P.L. et al. An oncogenic role for the multiple endocrine neoplasia type 1 gene in prostate cancer. Prostate Cancer Prostatic Dis 12, 184-91 (2009).
13. Volik, S. et al. End-sequence profiling: sequence-based analysis of aberrant genomes. Proc Natl Acad Sci U S A 100, 7696-701 (2003).
14. Bashir, A., Volik, S., Collins, C., Bafna, V. & Raphael, B.J. Evaluation of paired-end sequencing strategies for detection of genome rearrangements in cancer. PLoS Comput Biol 4, e1000051 (2008).
15. Chin, K. et al. Genomic and transcriptional aberrations linked to breast cancer pathophysiologies. Cancer Cell 10, 529-41 (2006).
16. Hajirasouliha, I. et al. Detection and characterization of novel sequence insertions using paired-end next-generation sequencing. Bioinformatics 26, 1277-83 (2010).
17. Alkan, C. et al. Personalized copy number and segmental duplication maps using next-generation sequencing. Nat Genet 41, 1061-7 (2009).
18. Hach, F. et al. mrsFAST: a cache-oblivious algorithm for short-read mapping. Nat Methods 7, 576-7.
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