Dataset DOI: 10.5061/dryad.x3ffbg80h

Description of the data and file structure

Bulk RNA sequencing of two separate datasets was performed to investigate transcriptional responses of human long head of the biceps tendon (LHBT) tissue and tendon-derived cells under different mechanical loading conditions. The first dataset (Kallisto_LHBT_Prox_Dist) includes RNA-seq data from frozen proximal and distal LHBT tissue samples collected during shoulder surgery. These samples were examined by a pathologist, histopathologically scored, and characterized for matrix composition and collagen crosslink content. The second dataset (Kallisto_TendonSheets) comprises RNA-seq data from primary human tendon-derived cells cultured in silicone chambers. After three days of culture with ascorbic acid supplementation, the cells were subjected to cyclic tensile stretching on day four using two distinct strain regimes (low-load and high-load).

Files and variables

File: Kallisto_LHBT_Prox_Dist.zip

Description: Folder containing transcript quantification results generated with the Kallisto package in R.
The folder includes a total of 61 files:

• 1 metadata.xlsx containing samples metadata
• 30 adundance.txt files with transcript-level abundance estimates (15 proximal and 15 distal samples)
• 30 adundance.h5 files, which are binary versions of the same quantification data for use in downstream analysis (e.g. tximport )

File: Kallisto_TendonSheets.zip

Description: Folder containing transcript quantification results generated with the Kallisto package in R.
The folder includes a total of 43 files:

• 1 metadata.xlsx containing samples metadata
• 21 adundance.txt files with transcript-level abundance estimates (representing 7 patients across 3 conditions — Control, High load, and Low load)
• 21 adundance.h5 files, which are binary versions of the same quantification data for use in downstream analysis (e.g. tximport )

Kallisto is a tool for quantifying transcript abundances from RNA-Seq data. Instead of performing full read alignment to the genome, Kallisto uses a pseudoalignment approach: it rapidly determines which transcripts each read is compatible with, without computing a base-by-base alignment. To do this, Kallisto relies on a prebuilt transcriptome index, which contains the k-mer–based representation of all reference transcripts (reference paper https://www.nature.com/articles/nbt.3519). 

Input files

As input files Kallisto accepts:

• paired-end reads: two FASTQ files per sample (sample_R1.fastq.gz, sample_R2.fastq.gz)
• single-end reads: one FASTQ file per sample plus fragment length information.

Output files

Kallisto produces several files in the output directory for each sample. These files contain transcript-level abundance estimates and metadata needed for reproducible analysis.

1. abundance.tsv — main quantification file

A tab-separated text file containing one row per transcript and the following columns:

• target_id: transcript identifier
• length: actual transcript length
• eff_length: “effective length” used to model fragment mapping
• est_counts: estimated number of fragments originating from this transcript
• tpm: transcripts per million (normalized expression)

How to reuse:

• Use est_counts for downstream differential expression (e.g., with DESeq2 via tximport).
• Use tpm for comparing expression levels across samples.

2. abundance.h5 — structured HDF5 version of the quantification

Binary file containing:

• the same values as abundance.tsv,
• plus additional information used by tools like Sleuth, including bootstrap estimates (if performed).

How to reuse:
Load directly in R using Sleuth (recommended for Kallisto-specific workflows).

3. run_info.json — metadata for reproducibility

This file records:

• Kallisto version
• Transcriptome index used
• Number of processed reads
• Fragment length parameters
• Number of threads
• Whether strand-specific or single-end settings were used

How to reuse:
This file allows users to replicate the exact Kallisto run.

Code/software

Library preparation and sequencing was performed by GENEWIZ® (Leipzig, Germany). In short, RNA integrity and yield were assessed with Agilent Fragment Analyzer (Agilent Technologies, USA). Total RNA was used to prepare the RNA-seq libraries with ribosomal depletion enrichment. Libraries were then sequenced on a NovaSeq 6000 sequencer (Illumina Inc, USA) and sequenced data were processed using Kallisto package in R  to generate a count file matrix for each individual sample.

Access information

Other publicly accessible locations of the data:

• NA

Data was derived from the following sources:

• NA - all data were generated in this study.