PEAKS 培訓課:AI模型在高通量蛋白質(zhì)組學中的應用
BSI 專注于蛋白質(zhì)組學與生物藥領(lǐng)域，依托機器學習與先進算法開發(fā)世界領(lǐng)先的質(zhì)譜數(shù)據(jù)分析軟件，助力加速生物學研究與藥物研發(fā)進程。
我們有幸受邀于10月10-11日承辦PEAKS Training Workshop，為您帶來深度、前沿的專業(yè)培訓，帶您走進深度蛋白質(zhì)組學的世界！歡迎各位老師的參與！
會議注冊地址：http://aohupo2025.com/register
(注冊時請選擇 PEAKS Online Training)
BSI, dedicated to proteomics and biopharmaceuticals, provides world-leading mass spectrometry data analysis software powered by machine learning and advanced algorithms to accelerate biological research and drug discovery.
We are honored to be invited to host the PEAKS Training Workshop on October 10–11, delivering in-depth, cutting-edge training to take you into the world of Deep Proteomics! We warmly welcome your participation!
Registered address：http://aohupo2025.com/register
(Remember to Choose PEAKS OnlineTraining)
Introduction
在過去的幾年中，大規(guī)模的蛋白質(zhì)組學研究使我們能夠開始在蛋白質(zhì)水平上了解表型。與人類基因組中大約 20,000 個蛋白質(zhì)編碼基因相比，人體內(nèi)有超過 100,000 種不同的蛋白質(zhì)，并且由于可變剪接以及轉(zhuǎn)錄后和翻譯后加工，還存在數(shù)十萬種 proteoform。多個課題研究表明蛋白質(zhì)形態(tài)比其相應的蛋白質(zhì)更好地描述蛋白質(zhì)水平的生物學并且是更具體的分化指標。人工智能（AI）模型的賦能帶來了高通量蛋白質(zhì)組學的革命。
本次為期兩天的 PEAKS 培訓課程，將介紹 AI 模型在高通量蛋白質(zhì)組學中的應用；展示如何針對多種互補的質(zhì)譜方法，結(jié)合數(shù)據(jù)庫搜索和從頭測序的數(shù)據(jù)分析，達到在proteoform水平上的深度覆蓋。無論是對于剛剛進入AI賦能的蛋白質(zhì)組領(lǐng)域的新手，還是有經(jīng)驗的大隊列樣本的蛋白質(zhì)組學研究，都非常適合參加這個培訓。
將LC-MS/MS數(shù)據(jù)與下一代測序（NGS）結(jié)果相結(jié)合，PEAKS 可實現(xiàn)全面的免疫肽組分析， 突破單一技術(shù)手段的局限，確保精準鑒定主要組織相容性復合體(MHC)結(jié)合肽，為癌癥免疫治療研究提供支持。
譜圖庫搜索與序列庫搜索是DIA數(shù)據(jù)分析的兩大核心方法。借助優(yōu)化后的從頭測序技術(shù)，PEAKS DIA 數(shù)據(jù)分析可實現(xiàn)蛋白質(zhì)組的深度覆蓋。
Spectral library search and library-free search are the two major approaches for DIA data analysis. Enhanced by de novo sequencing, PEAKS DIA data analysis provides you deep coverage.
#利用 PEAKS 從 DIA 數(shù)據(jù)中挖掘肽組與蛋白質(zhì)組“暗物質(zhì)” Uncover dark peptidome/proteome from DIA data with PEAKS
與只能檢測糖基是否存在的通用工具不同，GlycanFinder 可實現(xiàn)糖鏈結(jié)構(gòu)解析（如連接方式、分支結(jié)構(gòu)），解決了在糖蛋白組學研究中表征復雜糖鏈結(jié)構(gòu)的關(guān)鍵挑戰(zhàn)。
Unlike generic tools that only detect glycan presence, GlycanFinder provides structural-level resolution of glycans (e.g., linkage, branching), solving the key challenge of characterizing complex glycan structures in glycoproteomics research.
#基于云服務器的大規(guī)模隊列蛋白質(zhì)組學擴展分析 Cloud-based scalable data analysis of large cohort proteomics.
PEAKS Online 云平臺可實現(xiàn)大規(guī)模隊列項目的自動化分析，支持深度蛋白質(zhì)組學與蛋白質(zhì)基因組學分析，并能滿足發(fā)現(xiàn)型與靶向型質(zhì)譜技術(shù)的分析通量需求。
作為生物信息學的領(lǐng)軍企業(yè)，BSI專注于蛋白質(zhì)組學和生物藥領(lǐng)域，通過機器學習和先進算法提供世界領(lǐng)先的質(zhì)譜數(shù)據(jù)分析軟件和蛋白質(zhì)組學服務解決方案，以推進生物學研究和藥物發(fā)現(xiàn)。我們通過基于AI的計算方案，為您提供對蛋白質(zhì)組學、基因組學和醫(yī)學的卓越洞見。旗下著名的PEAKS®️系列軟件在全世界擁有數(shù)千家學術(shù)和工業(yè)用戶，包括：PEAKS®️ Studio，PEAKS®️ Online，PEAKS®️ GlycanFinder, PEAKS®️ AB，，ProteoformXTM ，DeepImmu®️免疫肽組發(fā)現(xiàn)服務和抗體綜合表征服務等。Email: sales-china@bioinfor.com 電話：021-60919891
本次為期兩天的 PEAKS 培訓課程，將介紹 AI 模型在高通量蛋白質(zhì)組學中的應用；展示如何針對多種互補的質(zhì)譜方法，結(jié)合數(shù)據(jù)庫搜索和從頭測序的數(shù)據(jù)分析，達到在proteoform水平上的深度覆蓋。無論是對于剛剛進入AI賦能的蛋白質(zhì)組領(lǐng)域的新手，還是有經(jīng)驗的大隊列樣本的蛋白質(zhì)組學研究，都非常適合參加這個培訓。
Over the past few years, large scale proteomics efforts have allowed us to begin to understand phenotype at a protein level. In contrast to about 20,000 protein coding genes in the human genome, there are over 100,000 different proteins and, as a result of alternative splicing, hundreds of thousands of protein isoforms (variants) in the human body. However, proteins function in the context of modifications that include alternative splicing and posttranscriptional and posttranslational processing. Proteoforms better describe protein-level biology and are more specific indicators of differentiation than their corresponding proteins. It comes the revolution in highthroughput proteomics with artificial intelligence (AI) models.
In PEAKS Online training workshop, we are going to introduce AI models for high throughput proteomics with deep coverage at proteoform level, by integrate alternative MS approaches with complementary database search and de novo sequencing data analysis. This training is highly suitable for both newcomers who have just entered the AI empowered proteomics field and experienced researchers engaged in proteomics studies of large cohort samples.
In PEAKS Online training workshop, we are going to introduce AI models for high throughput proteomics with deep coverage at proteoform level, by integrate alternative MS approaches with complementary database search and de novo sequencing data analysis. This training is highly suitable for both newcomers who have just entered the AI empowered proteomics field and experienced researchers engaged in proteomics studies of large cohort samples.
# 基于深度學習的從頭測序與數(shù)據(jù)庫搜索：助力深度、高通量蛋白質(zhì)組學研究 Deep learning-based de novo sequencing and database search for in-depth and high-throughput proteomics
整合數(shù)據(jù)庫搜索與從頭測序技術(shù)，并結(jié)合多種酶解策略，可實現(xiàn)蛋白質(zhì)組的深度覆蓋。覆蓋度可達到1.5萬個蛋白組中的100萬條獨特肽段，平均序列覆蓋率約為80%。
Integrating database search and de novo sequencing and combining multiple enzyme digests facilitated deeper proteome coverage up to a million unique peptides from 15,000 protein groups, with a median sequence coverage of approximately 80%.
# 高可信度翻譯后修飾（PTM）與序列變異分析 Confident PTM and Sequence Variants Profiling借助基于人工智能驅(qū)動的評分模型，PEAKS在鑒定翻譯后修飾（PTM）與序列變異時可實現(xiàn)超高可信度，有效解決了在低豐度修飾或序列變異檢測中常見的靈敏度與準確性問題。
Leveraging AI-powered scoring models, PEAKS achieves ultra-high confidence in identifying post-translational modifications (PTMs) and sequence variants, addressing the common issue of sensitivity and accuracy in low-abundance modification or variant detection.
#多組學驅(qū)動的高準確度、高靈敏度免疫肽組分析 Multi-omics enabled immunopeptidome with higher accuracy and sensitivity將LC-MS/MS數(shù)據(jù)與下一代測序（NGS）結(jié)果相結(jié)合，PEAKS 可實現(xiàn)全面的免疫肽組分析， 突破單一技術(shù)手段的局限，確保精準鑒定主要組織相容性復合體(MHC)結(jié)合肽，為癌癥免疫治療研究提供支持。
Integrating LC-MS/MS data with next-generation sequencing (NGS) results, PEAKS enables comprehensive immunopeptidome profiling—overcoming the limitation of single-technology approaches and ensuring accurate identification of MHC-bound peptides for cancer immunotherapy research.
#基于DIA的高效蛋白質(zhì)組學工作流程 Stream-lined Proteomics Proteomics Workflow with DIA譜圖庫搜索與序列庫搜索是DIA數(shù)據(jù)分析的兩大核心方法。借助優(yōu)化后的從頭測序技術(shù)，PEAKS DIA 數(shù)據(jù)分析可實現(xiàn)蛋白質(zhì)組的深度覆蓋。
Spectral library search and library-free search are the two major approaches for DIA data analysis. Enhanced by de novo sequencing, PEAKS DIA data analysis provides you deep coverage.
#利用 PEAKS 從 DIA 數(shù)據(jù)中挖掘肽組與蛋白質(zhì)組“暗物質(zhì)” Uncover dark peptidome/proteome from DIA data with PEAKS
除譜圖庫搜索與直接數(shù)據(jù)庫搜索外，我們提出一種新型計算方法，可直接從復雜 DIA圖譜中鑒定肽序列變異體與新肽段，同時嚴格控制假發(fā)現(xiàn)率（FDR）。
In addition to library search and direct database search, we present a novel computational method to identify peptide sequence variants and novel peptides directly and solely from complex DIA spectra while rigorously controlling the false discovery rate.
#結(jié)合Intact,Top-Down與Bottom-Up技術(shù)的深度Proteoform分析 Deep Proteoform Profiling with Intact, Top-Down and Bottom-Up通過整合Intact、Top-Down(TD)，與Bottom-Up(BU)技術(shù)，PEAKS可實現(xiàn)全面的Proteoform表征（包括亞型、翻譯后修飾組合），這一突破解決了單一方法工具無法充分解析蛋白質(zhì)組復雜性的難題。
By integrating intact protein analysis, Top-Down (TD), and Bottom-Up (BU) approaches, PEAKS enables comprehensive proteoform characterization (including isoforms, PTM combinations)—a breakthrough for understanding proteome complexity that single-method tools cannot achieve.
#PEAKS GlycanFinder 實操演示 — 具有結(jié)構(gòu)分辨率的糖譜學分析 PEAKS GlycanFinder Walkthrough - Glycan Profiling with Structural與只能檢測糖基是否存在的通用工具不同，GlycanFinder 可實現(xiàn)糖鏈結(jié)構(gòu)解析（如連接方式、分支結(jié)構(gòu)），解決了在糖蛋白組學研究中表征復雜糖鏈結(jié)構(gòu)的關(guān)鍵挑戰(zhàn)。
Unlike generic tools that only detect glycan presence, GlycanFinder provides structural-level resolution of glycans (e.g., linkage, branching), solving the key challenge of characterizing complex glycan structures in glycoproteomics research.
#基于云服務器的大規(guī)模隊列蛋白質(zhì)組學擴展分析 Cloud-based scalable data analysis of large cohort proteomics.
PEAKS Online 云平臺可實現(xiàn)大規(guī)模隊列項目的自動化分析，支持深度蛋白質(zhì)組學與蛋白質(zhì)基因組學分析，并能滿足發(fā)現(xiàn)型與靶向型質(zhì)譜技術(shù)的分析通量需求。
PEAKS Online enables the automation of large-scale cohort project with in-depth proteomics and proteogenomics analyses and throughput of discovery and targeted mass-spectrometry-based technologies.
#蛋白質(zhì)從頭測序 Protein de novo Sequencing
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