LNCRNA Illumina sequencing results associated with renal fibrosis disease progression
Data files
Jun 06, 2024 version files 91.05 GB
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C2_1.fq.gz
3.40 GB
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C2_2.fq.gz
3.47 GB
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C4_1.fq.gz
3.63 GB
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C4_2.fq.gz
3.68 GB
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C6_1.fq.gz
4.24 GB
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C6_2.fq.gz
4.37 GB
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M21_1.fq.gz
3.89 GB
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M21_2.fq.gz
4 GB
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M22_1.fq.gz
3.23 GB
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M22_2.fq.gz
3.26 GB
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M23_1.fq.gz
4.02 GB
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M23_2.fq.gz
4.11 GB
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M41_1.fq.gz
3.52 GB
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M41_2.fq.gz
3.57 GB
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M42_1.fq.gz
4.01 GB
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M42_2.fq.gz
4.12 GB
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M43_1.fq.gz
4.08 GB
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M43_2.fq.gz
4.27 GB
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M61_1.fq.gz
3.47 GB
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M61_2.fq.gz
3.58 GB
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M62_1.fq.gz
3.96 GB
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M62_2.fq.gz
4.06 GB
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M63_1.fq.gz
3.50 GB
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M63_2.fq.gz
3.63 GB
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rawdata_MD5.txt
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README.md
3.59 KB
Abstract
Objective: To screen and identify long noncoding RNAs (lncRNAs) involved in the progression of renal fibrosis and to explore their functions. Methods: We used unilateral ureteral obstruction (UUO) to establish a rat model of renal fibrosis. The animals were randomly divided into four groups: control, model group at 2 weeks (MOD-2), MOD-4, and MOD-6. Renal function was monitored by measuring the levels of conventional biochemical biomarkers. Differentially expressed lncRNAs (DE-lncRNAs) between the control and model groups were screened, and their functions were annotated using Gene Ontology and Kyoto Encyclopedia of Genes and Genomes analysis. Gene interaction networks were visualized using Cytoscape.
Results: Transcriptomic analysis revealed 243 DE-lncRNAs (82 upregulated, 161 downregulated) in the MOD-2 group compared with the control group, 216 DE-lncRNAs (95 upregulated, 121 downregulated) in the MOD-4 group, and 70 DE-lncRNAs (39 upregulated, 31 downregulated) in the MOD-6 group. Compared with the control group, the model groups were enriched in target genes involved in extracellular matrix–receptor interaction, arginine and proline metabolism, arachidonic acid metabolism, glutathione metabolism, peroxisome proliferator-activated receptor signaling, Janus kinase–signal transducer and activator of transcription signaling, phosphoinositide-3-kinase–Akt signaling, and calcium signaling.
Conclusion: We speculate that the six upregulated DE-lncRNAs in the model groups may be involved in the occurrence and development of renal fibrosis, while the seven downregulated DE-lncRNAs may have inhibitory effects
https://doi.org/10.5061/dryad.g4f4qrfxw
The original image data files obtained by Illumina HiSeq sequencing platform during rat renal fibrosis process were converted into Sequenced Reads by Base Calling analysis. We call this Raw Data or Raw Reads.
Description of the data and file structure
1 and 2 in the file name represent Paired-end sequence files respectively
Description of the result file:
The fq.gz file is the original sequencing sequence of high throughput sequencing, and the results are stored in FASTQ file format. Inclusive test
Sequence information of sequence sequence and corresponding sequencing quality information. Each read in the FASTQ file is described by four lines,
Its format is as follows:
@HWI-ST1276:71:C1162ACXX:1:1101:1208:2458 2:N:0:CGATG
CTGGCTCCGGAGGGGATGGAGGCGGCACTCCCGCCAAGGATGCGTTG...
+
BCBFFFFDHHHHHJJ? EAGIIIAHIJIIGHHHBEDCDDD; >>BD? BDAD<><? B...
The first line begins with "@", followed by Illumina Sequence Identifiers and
Description text (optional part);
The second row is the base sequence;
The third line begins with a "+", followed by the Illumina sequencing identifier (optional part);
The fourth line is the sequencing quality of the corresponding base, and the ASCII value of each character in the line is subtracted by 33, which is
The sequencing quality value corresponding to the second row base.
Illumina Sequence Identifiers details are as follows:
HWI-ST1276: Unique instrument name
71: Run ID
C1162ACXX Flowcell: ID
1: Flowcell lane
1101: Tile number within the flowcell lane
1208: 'x'-coordinate of the cluster within the tile
2458: 'y'-coordinate of the cluster within the tile
2: Member of a pair, 1 or 2 (paired-end or mate-pair reads only)
N: Y if the read fails filter (read is bad), N otherwise
0: 0 when none of the control bits are on, otherwise it is an even number
CGATGT: Index sequence
rawdata_MD5.txt: The main uses of MD5 files include file integrity verification, data consistency check, security protection, file identification and repeatability detection. 12 File integrity verification: By comparing the MD5 values before and after the verification, you can verify file integrity to ensure that the file is not tampered with or damaged. This is especially important during data transfer and storage, as there can be transmission errors, network interference, or disk failures that cause data errors. The MD5 check on transmitted or stored data ensures data consistency among different nodes. Data consistency check: MD5 check can also be used to verify data consistency. During data transmission and storage, various conditions may cause data errors. By performing MD5 check on data, you can detect and correct these errors in time to protect data security. Security protection: MD5 checksum can also be used to detect malicious tampering. If a file is maliciously tampered with during transmission, the MD5 code of the file will also change accordingly. By checking the MD5 value, you can detect file tampering in time to protect data security. File recognition and repeatability detection: Each file has a unique MD5 code, and the MD5 code of different files is almost different. Therefore, MD5 codes can be used for file recognition and repeatability detection. By comparing the MD5 codes of files, you can determine whether two files are identical, avoid storing and transferring duplicate files, and save storage space and transmission bandwidth.