1. Basic situation of the project

1. Project name: Protein data analysis service

2. Project number: EBS26081400002

3. Project location: Jinfeng Laboratory

4. Procurement method: inquiry

2. Procurement content

3. Business requirements

1. Delivery time: Complete all experiments and data analysis work and complete acceptance within 30 days after signing the contract.

2. Supply/service location: Jinfeng Laboratory, No. 313 Jinyue Road, Chongqing High-tech Zone；

3. Payment method: Settlement will be carried out after the completion report is delivered and the acceptance is passed. After formal settlement, the entire payment will be paid in one go.

4. Total project price limit: The total price quoted shall not be higher than the limit price of 199,688 yuan. The quoted price is a comprehensive lump sum price, including all equipment fees, software fees, installation fees, testing fees, travel expenses, freight, material fees, taxes, etc. The purchaser will not pay other fees separately.

4. Project requirements

Party B shall provide proteomics bioinformatics analysis services covering data verification, quality control, data cleaning and standardization, statistical analysis, function and pathway analysis, in-depth data mining, clinical correlation, statistical modeling, machine learning, external validation, mechanism integration and delivery of scientific research-grade results based on the original proteomics data, identification and quantitative results, protein expression matrix and related clinical/phenotypic information provided by the purchaser.

The analysis plan should be customized based on the purchaser's actual scientific research problems and experimental design, and should not only provide fixed templates or software to automatically generate reports.

Project specific requirements:

 (1) Data reception and quality assessment

1. Verify the integrity and consistency of the data files, sample numbers, grouping information and clinical/phenotypic information of 436 samples.

2. Conduct a systematic evaluation of the number of protein identifications, quantitative completeness, proportion of missing values, correlation between samples, data distribution and abnormal samples.

3. Carry out PCA, hierarchical clustering and necessary batch effect analysis to identify abnormal samples, potential confounding factors and batch bias.

4. Form a problem list for the discovered data anomalies, sample anomalies or information inconsistencies, and propose a solution.

 (2) Data cleaning and standardization

1. Complete the filtering of contaminating proteins, reverse sequences, low-confidence proteins and other unsuitable analysis items based on data type.

2. Carry out data transformation, normalization, missing pattern assessment and necessary missing value processing.

3. Carry out necessary batch effect assessment and correction based on experimental design and data characteristics.

4. Compare the data before and after processing to ensure that the data cleaning and standardization process is traceable.

5. All data processing methods should provide a clear basis, and large-scale data filling or manual modification should not be performed without evaluation.

 (3) Basic statistics and difference analysis

Complete at least:

 Protein identification and quantitative statistics；

 Sample correlation analysis；

 PCA and hierarchical clustering；

 Abnormal sample identification；

 Intergroup and paired differential protein analysis；

 Calculation of Fold Change, P value and FDR/q value；

 Differential protein volcano plot, heat map, box plot/violin plot and expression trend analysis.

For paired, multifactor, repeated measurements, or data with clinical confounders, a statistical model that matches the study design should be used, and adjustments should be made for age, sex, batch, and other clinical factors as necessary.

 (4) Function and pathway analysis

Based on the research questions:

 GO functional enrichment；

 KEGG pathway analysis；

 Reactome pathway analysis；

 Hallmark gene set analysis；

 ORA and GSEA；

 GSVA/ssGSEA or functional module scoring；

 PPI protein interaction network analysis；

 Screening of key functional modules and pathways.

Functional explanation should integrate the direction of protein change, effect size, statistical significance and functional relationship, and should not form a conclusion based only on a single enrichment P value.

 (5) Deep data mining

Carry out according to the scientific research needs of the purchaser:

1. Protein co-expression and functional module analysis；

2. WGCNA network analysis；

3. Unsupervised classification such as NMF and Consensus Clustering；

4. Screening of molecular subtypes and subtype characteristic proteins；

5. pathway/module score construction；

6. Screening of key proteins and network central nodes；

7. biomarker/signature screening；

8. Correlation analysis between candidate proteins and clinical/pathological phenotypes；

9. Multi-factor adjustment and analysis of potential confounding factors；

10. Carry out other customized analyzes based on actual scientific research issues.

 (6) Statistical modeling and machine learning

According to the research purpose, LASSO/Elastic Net, random forest, SVM, XGBoost or other appropriate algorithms are used to carry out feature screening and prediction model construction.

The model should use cross-validation, Bootstrap, training set/validation set division or independent verification to evaluate stability, and strictly avoid information leakage and over-fitting.

Classification models in principle provide:

ROC/AUC, PR-AUC, sensitivity, specificity, accuracy, confusion matrix and necessary calibration evaluation.

When it comes to survival outcomes, proceed according to data conditions:

Kaplan-Meier, Cox regression, C-index and time-dependent ROC analysis.

 (7) Integration of key proteins and mechanisms

Carry out correlation, co-expression, functional enrichment, PPI network, network topology and clinical phenotype correlation analysis on important candidate proteins to construct:

Key protein → Functional module → Biological pathway → Phenotype/clinical characteristics

multi-level association links.

The analysis should not stop at "differential proteins + a single pathway", but should combine multiple statistical and functional analysis levels to screen for candidate proteins and mechanisms that are stable and have biological explanation capabilities.

 (8) External data verification

For important key proteins, signatures, molecular subtypes or prediction models, if public data conditions permit, public proteomics databases, independent public cohorts or other appropriate external data should be used for verification.

When necessary, cross-omics supportive verification can be carried out using public transcriptome, single-cell omics and other data, and the consistency and robustness of the discovery cohort and validation cohort can be evaluated.

 (9) Analysis depth requirements

The overall analysis should form a complete scientific research logic:

Data quality control → Difference discovery → Stable feature screening → Functional module analysis → Pathway and network analysis → Key protein screening → Clinical/phenotypic correlation → Model construction → Internal/external validation → Integration of biological mechanisms.

The final results should be able to support the writing of scientific research papers, project research, screening of candidate markers and subsequent experimental verification.

 (10) Result delivery and technical services

Party B shall at least deliver:

1. Data quality assessment and abnormal sample reporting；

2. Protein quantification matrix before and after cleaning and normalization；

3. All difference analysis and statistical results table；

4. Functional analysis results of GO, KEGG, Reactome, GSEA, PPI, etc.；

5. In-depth data mining and clinical correlation analysis results；

6. Statistical model and machine learning model results；

7. External verification and mechanism integration results；

8. Source data corresponding to all drawings；

9. PDF/SVG vector images and ≥300 dpi high-definition TIFF/PNG images；

10. Software name, version, database version and key analysis parameters；

11. R/Python analysis script or equivalent reproducible analysis process；

12. Complete project analysis report and methodology description.

After the routine analysis of the project is completed, no less than 3 rounds of personalized data analysis, result review or chart optimization services should be provided according to the scientific research needs of the purchaser.

 (11) Acceptance and data security

1. The data correspondence, quality control, statistics and advanced analysis results of the 436 samples should be complete.

2. The core analysis results should be traceable to the original input data, and the analysis methods, parameters and processes should be repeatable and reproducible.

3. In-depth analysis, model construction, external verification and personalized analysis should meet the agreed requirements and cannot be replaced by regular automated analysis reports.

4. Unqualified results caused by errors in Party B’s data processing, statistical methods, program codes or analysis processes shall be re-analyzed or corrected free of charge.

5. The analytical data, charts, models and derivative results generated by the project belong to the purchaser.； Without the written permission of the purchaser, it shall not be disclosed, transferred or used for other research, commercial databases and algorithm/artificial intelligence model training to third parties.

5. Supplier qualification requirements

Meet the qualifications of Article 22 of the "Government Procurement Law of the People's Republic of China":

1. Have the ability to independently bear civil liability.

2. Have good business reputation and sound financial accounting system.

3. Have the necessary equipment and professional technical capabilities to perform the contract.

4. Have a good record of paying taxes and social security funds in accordance with the law.

5. In the three years before participating in government procurement activities, there should be no major illegal records in business activities.

6. Other conditions stipulated by laws and administrative regulations.

6. Inquiry schedule

1. Inquiry release time: 16:00 on August 26, 2026

2. Inquiry deadline: 16:00 on August 31, 2026

Suppliers should encrypt and compress the quotation list and related attachments (which need to be stamped with the official seal and scanned in color) into a zip format before the quotation deadline, and only send the encrypted compressed package to the Jinfeng Laboratory procurement email jfsys-caigou@jflab.ac.cn. The decompression password and project quotation amount must not be entered in the body of the email, otherwise it will be treated as an invalid quotation. The password will be notified separately after the expiration date by the purchaser. Those who do not quote before the quotation deadline will be deemed to have given up automatically. The subject of the email must be filled in strictly in accordance with the format: [Full name of the project] + Supplier's complete unit name + Quotation document. If the subject is filled in incorrectly, causing the purchaser to be unable to identify and classify the email, and the risk of the email being misjudged as spam by the system is borne by the supplier.

7. Quotation document requirements

1. The supplier's quotation document should include the following attachments (see Part 2 for details):

(1) Quotation letter

(2) Quotation list

(3) Legal representative qualification certificate (if the bidder is an authorized representative, he must also provide a legal representative authorization letter)

(4) Statement

(5) Letter of commitment

(6) Business license or other organization certification documents

2. The bidder should prepare a freshly stamped quotation document (PDF) and send it to the mailbox. The quotation document must be clearly visible, otherwise the quotation will be deemed invalid.

3. The quotation document should be produced in accordance with the requirements of the "Purchase Announcement". The quotation document must be signed and sealed by the legal representative of the bidder or his authorized representative at the prescribed signature place. The signature, seal and content should be complete. If there are any omissions, you will bear the risk of being deemed an invalid quotation.；

4. The quotation document should be written clearly and neatly. Any interline insertions, alterations, additions or deletions must be signed or stamped with the personal seal of the bidder’s legal representative or his authorized representative. Quotation documents that are illegible, unclearly expressed, or may lead to non-unique understanding will be deemed invalid.

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