Engineered probiotic restores glp-1 signaling to ameliorate fiber-deficiency-exacerbated colitis
Data files
Oct 03, 2025 version files 140.58 MB
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README.md
18.29 KB
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Transcriptomics_data_and_QC.zip
140.57 MB
Abstract
Raw sequencing data were first processed through Novogene’s quality control (QC) pipeline, in which raw reads were filtered to remove adapter sequences, ambiguous bases (N > 10%), and low-quality reads (reads with >50% bases having a quality score ≤ 5). Clean reads obtained from this step were then aligned to the reference genome using HISAT2, allowing precise mapping of splice junction reads and comprehensive detection of differential gene expression, alternative splicing events, genetic variants, and gene fusions. Post-mapping analyses included quantification and annotation of gene expression levels, normalized as fragments per kilobase of transcript per million mapped reads (FPKM). Genes were annotated with detailed genomic attributes such as chromosome location, strand orientation, gene length, biotype, transcription factor families, and functional descriptions. All QC information, stats and analysis including reads count and FPKM used for further analysis are uploaded here.
Transcriptomics_data_and_QC.zip
SupFile
1 Directory tree
SupFile
|--all_compare.xls [gene expression and annotation information]
|--gene.fa [fasta sequence file of gene]
|--Upstream_Geneseq.fa [fasta sequence file of upstream]
|--Downstream_Geneseq.fa [fastq sequence file of downstream]
|--CDS.fa [fasta sequence file of cds]
|--gene.xls [gene-related information file]
|--go.xls [go information about related gene]
|--kegg.xls [kegg information about related gene]
|--ko.xls [KO annotation about related gene]
|--ppi.xls [ppi information about related gene]
|--SupFile/GOclassification
|--1.GO_classification_bar.png [the figure of classification statistics by the GO annotation, all genes annotated to the second level are mapped, png]
|--2.GO_classification_bar.pdf [the figure of classification statistics by the GO annotation, all genes annotated to the second level are mapped, pdf]
|--3.GO_classification.xls [the classified statistical results of GO annotations]
|--4.GO_classification_count.txt [the input data of the figure for classification statistics by the GO annotation]
|--5.GO_classification.dat [the corresponding files of go id and gene id]
2 File Formats Specification
2.1 all_compare.xls
Tab-delimited text file can be opened in Excel. The file format definition is as follows:
| column | names | description |
|---|---|---|
| 1 | gene_id | Gene ID |
| 2 | sample_count | The readcount of genes of sample |
| 3 | sample_fpkm | The FPKM of genes of sample |
| 4 | compare_treat | The mean value of normalised readcounts in treat group |
| 5 | compare_control | The mean value of normalised readcounts in control group |
| 6 | compare_log2FoldChange | log2FoldChange value of a comparison group |
| 7 | compare_pvalue | pvalue value of a comparison group |
| 8 | compare_padj | padj value of a comparison group |
| 9 | gene_name | Gene symbol |
| 10 | gene_chr | The chromosome of gene |
| 11 | gene_start | The start site of gene |
| 12 | gene_end | The end site of gene |
| 13 | gene_strand | The direction of the gene chain |
| 14 | gene_length | The length of the gene |
| 15 | gene_biotype | The biotype of gene |
| 16 | gene_description | The description of gene |
| 17 | tf_family | The transcription factor of gene |
2.2 gene.xls
Tab-delimited text file can be opened in Excel. The file format definition is as follows:
| column | names | description |
|---|---|---|
| 1 | gene_id | Gene ID |
| 2 | gene_name | Gene symbol |
| 3 | gene_chr | The chromosome of gene |
| 4 | gene_start | The start site of gene |
| 5 | gene_end | The end site of gene |
| 6 | gene_strand | The direction of the gene chain |
| 7 | gene_length | The length of the gene |
| 8 | gene_biotype | The biotype of gene |
| 9 | gene_description | The description of gene |
| 10 | tf_family | The transcription factor of gene |
2.3 go.xls
Tab-delimited text file can be opened in Excel. The file format definition is as follows:
| column | names | description |
|---|---|---|
| 1 | gene_id | Gene ID |
| 2 | go_id | Id of gene ontology |
| 3 | go_ontology | The gene ontology |
| 4 | go_term | The information of gene ontology |
2.4 kegg.xls
Tab-delimited text file can be opened in Excel. The file format definition is as follows:
| column | names | description |
|---|---|---|
| 1 | gene_id | Gene ID |
| 2 | pathway_gene_id | Gene id in kegg pathway database |
| 3 | pathway_id | Id of kegg pathway |
| 4 | pathway_name | The information of gene pathway |
2.5 ko.xls
Tab-delimited text file can be opened in Excel. The file format definition is as follows:
| column | names | Description |
|---|---|---|
| 1 | gene_id | Gene ID |
| 2 | ko_id | Kegg pathway id |
| 3 | ko_name | Kegg pathway name |
| 4 | ko_description | The description of kegg pathway |
2.6 ppi.xls
Tab-delimited text file can be opened in Excel. The file format definition is as follows:
| column | names | description |
|---|---|---|
| 1 | gene_id | Gene ID |
| 2 | protein_id | Id of protein |
2.7 GO_classification.xls
Tab-delimited text file can be opened in Excel. The file format definition is as follows:
| column | names | description |
|---|---|---|
| 1 | GO ID(Lev1) | GO id of level 1 |
| 2 | GO Term(Lev1) | GO term of level 1 |
| 3 | GO ID(Lev2) | GO id of level 2 |
| 4 | GO Term(Lev2) | GO term of level 2 |
| 5 | GO ID (Lev3) | GO id of level 3 |
| 6 | GO Term(Lev3) | GO term of level 3 |
| 7 | GO ID (Lev4) | GO id of level 4 |
| 8 | GO Term(Lev4) | GO term of level 4 |
| 9 | Gene Number | Gene number related to GO (Lev4) |
| 10 | Gene List | Gene id related to GO (Lev4) |
QC
1 Introduction
The Sequenced Reads/raw reads often contain low quality reads or reads with adapters, which will affect the analysis quality and reliability.To avoid this, it's necessary to fiIter the raw reads and get the clean reads. The procedure of raw reads filtering is as follows:
(1) Remove reads containing adapters;
(2)Remove reads containing $\mathrm {N}>10%$ (N represents base that could not be determined);
(3) Remove low quality reads: The Qscore (Quality value) of over 50% bases of the read $is<=5.$
2 Directory tree
QC/
QC/1.Error [Error Rate Distribution]
|-- sampleID_Error.pdf [figure of error rate distribution, pdf format]
|--sampleID_Error.pdf [figure of error rate distribution, png format]
|--sampleID_Error.svg [figure of error rate distribution, svg format]
QC/2.GC [GC Content Distribution]
|--sampleID_GC.pdf [figure of GC distribution diagram, pdf format]
|--sampleID_GC.pdf [figure of GC distribution diagram, png format]
|--sampleID_GC.svg [figure of GC distribution diagram,svg format]
QC/3.Filter [Data Filtering]
|--sampleID_Filter.pdf [figure of raw reads classification distribution,pdf format]
|--sampleID_Filter.pdf [figure of raw reads classification distribution, png format]
|--sampleID_Filter.png [figure of raw reads classification distribution, png format]
QC/4.Stat [Data Quality Summary]
|--data_table.xls [Summary table of sample data quality]
3 File Formats Specification
3.1 data_table.xls
The data quality of all samples are summaried in this table, which contains:
| column | names | description |
|---|---|---|
| 1 | sample | SampleID |
| 2 | library | LibraryID |
| 3 | raw_reads | Reads count from the raw data, four rows as an unit, with statistics of reads count for every sequencing |
| 4 | raw bases | Base number of raw data. (number of raw reads) * (sequence length), converting unit to G |
| 5 | clean reads | Clean data is reads count filtered from raw data. Statistics method is similar with raw reads.All the following analysis is based on clean data |
| 6 | clean bases | Base number of raw data after filtering. (number of clean reads) * (sequence length), converting unit to G |
| 7 | error_rate | Base error rate of whole sequencing |
| 8 | Q20 | Phred values greater than 20 base number contain the percentage of total bases. (Base number of Phred valu $\text {u}>2)/$ $(Totalbasenumber)*100$ |
| 9 | Q30 | Phred values greater than 30 base number contain the percentage of total bases. (Base number of Phred valu $\text {u}>3)/$ $(Totalbasenumber)*100$ |
| 10 | $GC_pct$ | The percentage of G&C base numbers of total bases.(G&C base number) / (Total base numbe $\text {r}*100$ |
Mapping
1 Introduction
Mapping the clean reads to the reference genome or the transcriptome is the basis for the next following analysis. We use HISAT2 to accomplish the mapping. If only the differential expression significant genes are needed, we map reads to the transcriptome directly. If the study of alternative splicing, variation, the fusion gene are needed, the alignment are carried out at the genome level. In order to avoid losing a lot of effective junction reads, we use HISAT2 software to alignment for RNA-seq sequencing data analysis and the junction reads can be positioned precisely.
2 Directory tree
Mapping/
Mapping/1.Region [Reads distribution on genome]
|--sampleID_Region.pdf [figures of Reads distribution on genome, pdf format]
|--sampleID_Region.png [figures of Reads distribution on genome, png format]
|--sampleID_Region.svg [figures of Reads distribution on genome, svg format]
Mapping/2.Stat [the summary of mapping result]
|--align_pct.xls [Statistical table of sample comparison rate]
|--align_region.xls [Statistical table of sample comparison area]
3 File Formats Specification
3.1 align_pct.xls
Tab-delimited text file can be opened in Excel. The file format definition is as follows:
| row | names | description |
|---|---|---|
| 1 | sample | sampleID. |
| 2 | total_reads | total clean reads using for analysis. |
| 3 | total_map | numbers of reads being mapped to the genome. |
| 5 | unique_map | numbers of reads being mapped to unique position of the genome. |
| 6 | multi_map | numbers of reads being mapped to multiple positions of the genome. |
| 7 | read1_map | number of left read that can be mapped tothe reference genome. |
| 8 | read2_map | number of right read that can be mapped to the reference genome. |
| 9 | positive_map | number of reads that can be mapped to the positive strand (+). |
| 10 | negative_map | number of reads that can be mapped to the minus strand (-). |
| 11 | splice_map | number of reads that can be segmented and mapped to two exons (alsonamed junction reads). |
| 12 | unsplice_map | number of reads thatcan be mapped entirely to a single exon. |
| 13 | proper_map | number of reads that can be mapped to the genome by paired readl and read2 |
3.2 align_region.xls
Tab-delimited text file can be opened in Excel. The file format definition is as follows:
| row | names | description |
|---|---|---|
| 1 | sample | sampleID. |
| 2 | exon | numbers of reads being mapped on the exon area. |
| 3 | intron | numbers of reads being mapped on the intron area. |
| 4 | intergenic | numbers of reads being mapped on the intergenic area. |
3.3 MappingRegion
Mapped regions can be classified as exon, intron, or intergenic region.
Exon-mapped reads should be the most abundant type of read when the reference genome is well-annotated. Intron-reads may be derived from pre-mRNA contamination or intron-retention events from alternative splicing. Reads mapped to intergenic regions are mainly because of weak annotation of the reference genome.
Quant
1 Introduction
The abundance of transcript reflects the gene expression level directly. In RNA-seq experiment, we estimate gene expression level by the abundance of transcripts(count of sequencing) that mapped to genome or exon. Reads count is proportional to gene expression level, gene length and sequencing depth. FPKM (short for the expected number of Fragments Per Kilobase of transcript sequence per Millions base pairs sequenced)is the most common method of estimating gene expression levels, which takes the effects into consideration of both sequencing depth and gene length on counting of fragments.
2 Directory tree
Quant/1.Count [gene expression level list]
|--gene_count.xls [the readcount list with the annotation of each gene]
|--gene_fpkm.xls [the FPKM list with the annotation of each gene]
|--gene_fpkm_group.xls [list of FPKM values for all groups. FPKM value: the adjusted readcount.(for biological replicates experiment)]
Quantification/2.Correlation [RNA-Seq Correlation]
|--correlation.xls [the list of pearson coefficients between samples]
Quantification/ 3.Distribution
|--fpkm_interval.xls [statistics list of expression level for all genes.The first column is the FPKM Interval, the other column is
the gene number(proportion) of different samples.]
3 File Formats Specification
3.1 gene_count.xls
Tab-delimited xls file can be opened in Excel. The file format definition is as follows:
| column | names | description |
|---|---|---|
| 1 | gene_id | Gene ID |
| 2 | sample | The readcount of genes of sample |
| 3 | gene_name | Gene symbol |
| 4 | gene_chr | Chromosome/scaffold ID |
| 5 | gene_start | Start coordinate |
| 6 | gene_end | End coordinate |
| 7 | gene_strand | Strand specificity |
| 8 | gene_length | Novel gene length |
| 9 | gene_biotype | The type of novel gene |
| 10 | gene_description | Description from PFAM Annotation |
| 11 tf_family | 11 tf_family | TheTFfamilyofnovelgene |
3.2 gene_fpkm.xls
Tab-delimited xls file can be opened in Excel. The file format definition is as follows:
| column | names | description |
|---|---|---|
| 1 | gene_id | Gene ID |
| 2 | sample | The FPKM of genesofsample |
| 3 | gene_name | Gene symbol |
| 4 | gene_chr | Chromosome/scaffold ID |
| 5 | gene_start | Start coordinate |
| 6 | gene_end | End coordinate |
| 7 | gene_strand | Strand specificity |
| 8 | gene_length | Novel gene length |
| 9 | gene_biotype | Thetypeofnovelgene |
| 10 | gene_description | Description from PFAM Annotation |
| 11 | tf_family | The TF family of novel gene |
| 1 | gene_id | Gene ID |
| 2 | group | The FPKM of genes of group |
| 3 | gene_name | Gene symbol |
| 4 | gene_chr | Chromosome/scaffold ID |
| 5 | gene_start | Start coordinate |
| 6 | gene_end | End coordinate |
| 7 | gene_strand | Strand specificity |
| 8 | gene_length | Novel gene length |
| 9 | gene_biotype | The type of novel gene |
| 10 | gene_description | Description from PFAM Annotation |
| 11 | tf_family | The TF family of novel gene |