9:00-9:10: 林仲彥  博士 (Chung-Yen Lin Ph.D.)

系統網路生物實驗室 (Lab of Systems Biology and Network Biology， http://eln.iis.sinica.edu.tw)

內容：簡介實驗室相關研究、生物資訊系統平台與資料庫等

Brief on Recently Research works， published web applications and database

1.電子實驗室記錄本/Elegance: Electronic Lab Notebook-- Digitize your experimental designs and results into wisdom from Discovery to Publication 

 9:10 - 9:40 黃智偉 先生 (Mr. Chi-Wei Huang)

系統網路生物實驗室 (Lab of Systems Biology and Network Biology， http://eln.iis.sinica.edu.tw)

內容: 電子實驗室記錄本之運用與維護

將實驗室中所產生的各種紀錄與大量數據，以電子化方式儲存於網路伺服器/一般桌上型/筆記型電腦，可藉由一般電腦、筆記型電腦與移動式裝置如智慧型手機與平板電腦等，透過一般的網路瀏覽器就可以來存取相關內容與研究數據，以高親和性介面，協助使用者管理、分享、搜尋、備份、列印，及與國內外研究伙伴線上討論相關問題，讓實驗室的眾多智慧、想法與研究歷程得以紀錄回顧與交互串連，不受時空與人員異動的影響，進而激盪出新的成果與方向。此一系統，除可供個人使用外，亦可建置成為一般實驗室內部與對外網站，並可作為國際合作研究平台，目前研究團隊正進行雲端版本的開發，與優化移動式設備(mobile devices)的存取介面，讓實驗室的智慧結晶得以快速存取與整合，並隨時上傳新的想法與心得，即時分享給研究伙伴，加速研究的進展。

平台： Electronic Laboratory Notebook (ELN) (影音簡介)

下載：Windows/ Mac， Linux/ Cloud version

Slides for this section: [PDF]

2. 次世代定序之線上基因概況分析平台/ Multi-Omics onLine Analysis System (MOLAS)

9:50 - 10:20 蘇聖堯 先生 (Mr. Sheng-Yao Su)/ 呂怡萱 小姐 (Ms. I-Hsuan Lu)

系統網路生物實驗室 (Lab of Systems Biology and Network Biology， http://eln.iis.sinica.edu.tw)

內容: 簡介運用於人類與小鼠等模式生物之次世代定序結果基因概況線上分析平台

Next generation sequencing technologies bring the gene profiling study into big-data science era. However, the increasing amount of data made itself a problem for viewing data and analyzing the biological implication from it.

Here we present MOLAS, Multi-Omics onLine Analysis System, a robust web service holding gene expression data with build-in annotations and a data analysis toolkit. MOLAS is composed by two parallel server daemons: MOLAS/pilot for project management, and MOLAS/harbor for hosting data retrieving and analysis website. Via an intuitive data loading process, a project is created on MOLAS/pilot. Then the uploaded data is forward to MOLAS/harbor, connected to the build-in annotations and data analysis pipeline, then turned into a website. MOLAS/harbor provides data accessing functions including full-text search, KEGG pathways and module hierarchy view, pairwise libraries comparison, clustering by user-defined scheme, and enrichment analysis in KEGG pathway and GO terms of differentially expressed genes identified by pairwise comparison or by clustering analysis.

Currently, MOLAS accepts gene expression data table in FPKM value, derived from Cufflinks or other tools that map reads to human reference (hg19) or mouse reference (mm10) and indexed in gene symbol. The website derived from a project can be a long-term hosted open-accessible website, or a private, password-controlled site for six months accession.

Website: http://molas.iis.sinica.edu.tw

3. 蛋白體學質譜儀資料定量分析工具之介紹/ Accurate Quantitation Tools for MS-based Proteomics

10:30 - 11:00： Ke-Shiuan Lynn Ph.D., 林可軒 博士(許聞廉、宋定懿老師團隊)

系統蛋白體實驗室 (Systems Proteomics Laboratory, IIS, Academia Sinica)

Abstract:

We developed two software packages, Multi-Q and IDEAL-Q, for accurate quantitation in MS-based proteomics studies. Multi-Q performs quantitation through MS2 data and is designed for iTRAQ labeling method (both 4-plex and 8-plex), whereas IDEAL-Q performs quantitation through MS1 data and is developed for label-free quantitation analysis. Both tools accommodate various input data formats from search engines and mass spectrometer manufacturers and can be used as generic platforms for protein/peptide quantitation. Our test results on large-scale experimental data showed that they not only provides fast and accurate protein/peptide quantitation but also are very robust to noise. In addition, both tools combined have been cited by over 110 articles and their web sites have been accessed by more than 6,000 visitors since 2006.

4. 通用性蛋白質細胞定位預測工具介紹/An Introduction of a Universal Protein Sub-cellular Localization Prediction Tool

11:10 -11:40 ：Hsin-Nan, Lin Ph.D., 林信男 博士  (許聞廉、宋定懿老師團隊)

系統蛋白體實驗室 (Systems Proteomics Laboratory, IIS, Academia Sinica)

Abstract:

Determination of protein subcellular localization (PSL) sites through wet-lab experiments is labor intensive and time consuming. We developed a computational approach, UniLoc, which is a universal PSL predictor based. UniLoc uses natural language processing techniques to define protein synonyms. A protein synonym is an n-gram peptide of amino acids that indicates a possible sequence variation in the evolution of a protein. We demonstrated that protein synonyms can help identify proteins with structural and functional similarity. UniLoc is built on a proteome-scale database, and it includes localization sites in prokaryotic and eukaryotic organisms. It can efficiently distinguish between single- and multi-localized proteins and make predictions with high precision and recall. UniLoc can also interpret a prediction with identified template sequences in the database. Comparing with the state-of-the-art PSL predictors, UniLoc is shown to be the most accurate PSL predictor.