Replace placeholder software entries with 19 Liu Lab tools

[Copilot]

Replaces generic placeholder software entries with actual Liu Lab software tools and establishes the assets directory structure for software images.

Changes

Removed

• soft-1.md and image-proc-sys.md placeholder files

Created 19 software entries

• Bioinformatics tools: MARRVEL, CB², SalmonTE, DASC, CRISPRcloud, CrypSplice, TCGA2STAT, XMRF, PARMESAN
• Spatial transcriptomics: MIST, RESORT, SEAGAL
• Multi-omics: MinNet, TREASMO, NMRQNet, SPA-STOCSY
• Computational algorithms: Combinatorial Therapy Discovery, Logit-Laplacian-net, DSA

Assets infrastructure

• Created src/assets/softwares/ directory
• Added README-IMAGES.md listing 13 image files to be manually uploaded from legacy website

Format

Each markdown file follows the schema defined in src/content.config.ts:

---
title: "MARRVEL"
developers: ["Liu Lab"]
link: "http://marrvel.org/"
date: 2026-01-19
description: "Model organism Aggregated Resources for Rare Variant ExpLoration..."
---

Content with image reference: ![MARRVEL Logo](../../assets/softwares/marrvel.png)

Note

Build will fail until images are uploaded per README-IMAGES.md instructions. Images reference URLs from the existing Liu Lab website.

Warning

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• telemetry.astro.build
  • Triggering command: /usr/local/bin/node node /home/REDACTED/work/Liuzlab.github.io/Liuzlab.github.io/node_modules/.bin/astro build (dns block)

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Original prompt

Objective

Update the software section by creating individual markdown files for each software tool and adding associated images to the assets folder.

Tasks

1. Remove existing placeholder software files

Delete the following placeholder files from src/content/softwares/:

• soft-1.md
• image-proc-sys.md

2. Create new software markdown files

Create the following markdown files in src/content/softwares/ directory:

marrvel.md

---
title: "MARRVEL"
developers: ["Liu Lab"]
link: "http://marrvel.org/"
date: 2026-01-19
description: "Model organism Aggregated Resources for Rare Variant ExpLoration - facilitates the use of public genetic resources to prioritize rare human gene variants for study in model organisms."
---

MARRVEL (Model organism Aggregated Resources for Rare Variant ExpLoration) aims to facilitate the use of public genetic resources to prioritize rare human gene variants for study in model organisms. To automate the search process and gather all the data in a simple display we extract data from human data bases (OMIM, ExAC, Geno2MP, DGV, and DECIPHER) for efficient variant prioritization. The protein sequences for six organisms (S. cerevisiae, C. elegans, D. melanogaster, D. rerio, M. musculus, and H. sapiens) are aligned with highlighted protein domain information via collaboration with DIOPT. The key biological and genetic features are then extracted from existing model organism databases (SGD, PomBase, WormBase, FlyBase, ZFIN, and MGI).

![MARRVEL Logo](../../assets/softwares/marrvel.png)

spa-stocsy.md

---
title: "SPA-STOCSY"
developers: ["Liu Lab"]
link: "https://github.com/LiuzLab/SPA-STOCSY"
date: 2023-09-01
description: "Spatial clustering algorithm – Statistical total correlation spectroscopy for identifying annotated and non-annotated metabolites in high-throughput NMR spectra."
---

SPA-STOCSY is an automated tool for identifying annotated and non-annotated metabolites in high-throughput NMR spectra. Metabolomics (the study of all metabolites in a given sample) can be a promising candidate for quantitative phenotyping and biomarker discovery. Nuclear magnetic resonance (NMR) is one of the main platforms used to acquire metabolomic data. To accelerate the application of NMR metabolomics in medicine and biology, we present SPA-STOCSY.

SPA exploits strong correlations among datapoints from the multiplets (multiple peaks belonging to the same metabolite) and identifies local spatial clusters of contiguous datapoints highly likely to arise from the same metabolite cluster. With the SPA-derived clusters as input, STOCSY is then used to highlight the correlation map and arrange clusters into highly correlated groups containing signals from the same metabolite. This work has been published in Bioinformatics (https://doi.org/10.1093/bioinformatics/btad593).

![SPA-STOCSY](../../assets/softwares/SPA-STOCSY.png)

cb2.md

---
title: "CB²"
developers: ["Liu Lab"]
link: "https://github.com/liuzlab/CB2"
date: 2026-01-19
description: "CRISPRBetaBinomial - provides functions for hit gene identification and quantification of sgRNA abundances for CRISPR pooled screen data analysis."
---

CB² provides functions for hit gene identification and quantification of sgRNA (single-guided RNA) abundances for CRISPR (Clustered Regularly Interspaced Short Palindromic Repeats) pooled screen data analysis.

salmonte.md

---
title: "SalmonTE"
developers: ["Liu Lab"]
link: "https://github.com/hyunhwaj/SalmonTE"
date: 2026-01-19
description: "Ultra-Fast and Scalable Quantification Pipeline of Transpose Element (TE) Abundances from Next Generation Sequencing Data."
---

SalmonTE is an ultra-Fast and Scalable Quantification Pipeline of Transpose Element (TE) Abundances from Next Generation Sequencing Data. It comes with Salmon which is a fast and accurate transcriptome quantification method.

dasc.md

---
title: "DASC"
developers: ["Liu Lab"]
link: "https://bioconductor.org/packages/3.6/bioc/html/DASC.html"
date: 2026-01-19
description: "Data-adaptive shrinkage and semi-Nonnegative Matrix Factorization (NMF) for the detection of unknown batch effects."
---

We introduce a new algorithm based on data-adaptive shrinkage and semi-Nonnegative Matrix Factorization (NMF) for the detection of unknown batch effects. We test our algorithm on three different datasets – 1) Sequencing Quality Control (SEQC), 2) Topotecan RNA-Seq and 3) Single-cell RNA-Seq on Glioblastoma Multiforme (GBM). We have demonstrated a superior performance in identifying hidden batch effects as compared to existing algorithms for batch detection in all three datasets. In the Topotecan study, we were able to identify a new batch factor that has been missed by the original study, leading to under-representation of differentially expressed genes. For scRNA-Seq, we demonstrated the power of our method in detecting subtle batch effects.

![DASC]...

</details>



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