OSCR

Investigating Alzheimer's Disease-Associated Genes Using Differential Splicing Frequency Analysis.

Code ↔ Paper

2 matches between paragraphs of the paper and lines of its authors' code, computed by the harvester (lexical-v1). Click a colored paragraph or line to see its counterpart.

The 2 matches
  1. [1] § 2. Materials and Methods › 2.1. Data Acquisition and Analysis ↔ bin/RNA-seq_clean.R, lines 1–143 · score 0.73 · low quality nucleotides, row sum, matrices, RNA seq, adapter, raw
  2. [2] § 2. Materials and Methods › 2.1. Data Acquisition and Analysis ↔ tools/Fq_statistics.R, lines 2–59 · score 0.52 · low quality nucleotides, Bytes, raw, sequences, fastq

Paper

Loaded from Europe PMC by your browser, not stored by OSCR: doi.org · Europe PMC

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The authors' code

R · 169 lines · 9.5 KB · no license · 1 match

  1. #这一版简化了质量分数计算，并且统一输出为Phred+33
  2. trimRead <- function(fastqfile, outfile, qualityCutoff, region5Len, region3Len, nCutoff, adapterMismatch, readLength, PCR2rc, RdPerYield)
  3. {
  4. trimmedFile <- gsub(pattern=".fastq", replacement=".trimmed3End", fastqfile);
  5. #不要后缀的文件名，注意变量名字不能是sample
  6. sampleName<-unlist(strsplit(fastqfile,'\\.|_'))[1];
  7. sampleDirection<-unlist(strsplit(fastqfile,'\\.|_'))[2];
  8. inFh <- FastqStreamer(fastqfile, n=RdPerYield);
  9. if (file.exists(outfile) ) {file.remove(outfile); } #如果输出文件已经存在必须删除，防止追加写
  10. total_nCount=0;
  11. raw_reads=0;
  12. raw_len=0;
  13. highQua_reads=0;
  14. trimmed_reads=0;
  15. trimmed_len=0;
  16. iteration=0;
  17. first_number=0;#给fastq文件中所有read的id上面加一个[编号]，从0开始计数
  18. while (batch_number <- length(reads <- yield(inFh))) { #每次控制读入5百万个reads
  19. iteration = iteration+1;
  20. #########################################
  21. ## Trim low quality nucleotides and Ns ##
  22. #########################################
  23. seqs <- sread(reads); # 取出所有记录（read）的sequence信息,数据格式DNAStringSet
  24. nCount<-alphabetFrequency(seqs)[,"N"];# 统计每条read中的字符（A,T,C,G,N）总数,提取"N"总数那一列,只用于输出
  25. total_nCount=total_nCount+sum(nCount);
  26. raw_reads=raw_reads+ length(reads);
  27. rm(nCount);
  28. if(iteration==1)#第一轮要自动判断分数系统的类型
  29. {
  30. score_sys = data.class(quality(reads));#如果出现字符";"（59）就是FastqQuality，否则就是SFastqQuality
  31. cat("the quality score system (SFastqQuality=Phred+64,FastqQuality=Phred+33) is",score_sys,"\n");
  32. raw_len <- max(width(reads));# 得到原始数据中reads长度
  33. }
  34. qual <- quality(quality(reads));#仅仅做格式转换,SFastqQuality或FastqQuality格式->BStringSet格式，qual还是字符表示形式
  35. myqual_16L <- charToRaw(as.character(unlist(qual)));#质量分数转为16进制表示，是一个很长的向量
  36. if(score_sys =="FastqQuality")#如果是Phred+33计分系统
  37. {
  38. myqual_10L <- strtoi(myqual_16L,16L)-33;#质量分数转为10进制表示
  39. }
  40. if(score_sys =="SFastqQuality")#如果是Phred+64计分系统，需要转为Phred+33计分系统
  41. {
  42. myqual_10L <- strtoi(myqual_16L,16L)-64;#质量分数转为10进制表示
  43. qual_temp <- PhredQuality (as.integer(myqual_10L));#质量分数转为Phred+33字符表示，一个大向量
  44. qual <- BStringSet(unlist(qual_temp), start= seq(from = 1, to = raw_len*(length(reads)-1)+1, by = raw_len), width=raw_len);
  45. rm(qual_temp);
  46. }
  48. last_number <- first_number + batch_number - 1; #本批数据最后一个read的编号
  49. #reads的id都要统一格式为sample_R1-number，为了下一步提取成对的reads使用，quality也统一为Phred+33计分系统
  50. reads <- ShortReadQ(sread=seqs, quality=qual, id= BStringSet(paste0(sampleName, "-", first_number:last_number, "/",sampleDirection) ) );
  51. first_number <- last_number + 1; #下批数据第一个read的编号，上一句使用后必须更新
  52. myqual_mat <- matrix(myqual_10L, nrow=length(qual), byrow=TRUE); #质量分数转为矩阵格式,为了产生at矩阵
  53. at <- myqual_mat < qualityCutoff;
  54. rm(myqual_16L);
  55. rm(myqual_10L);
  56. rm(qual);
  57. rm(myqual_mat);
  59. #下面3行将序列中所有质量低于阈值的核苷酸替换为"N",得到替换后的数据injectedseqs
  60. letter_subject <- DNAString(paste(rep.int("N", raw_len), collapse=""));
  61. #得到一个DNAString"对象,只包括一个"N"组成的向量,长度为读长的长度
  62. letter <- as(Views(letter_subject, start=1, end=rowSums(at)), "DNAStringSet");
  63. #每行都对应一个N组成的向量,长度等于低质量（小于阈值的）核苷酸的数量
  64. injectedseqs <- replaceLetterAt(seqs, at, letter);#injectedseqs是所有低质量核苷酸都被"N"替换后得到的所有read的序列
  65. #seqs中每行数据与at中每行数据对应,at中某个位置为TRUE的,seqs中对应位置的核苷酸替换为letter中的核苷酸"N"
  66. #特别注意这里的TRUE,表示该位点的质量分数小于前面的qualityCutoff
  67. rm(at);
  68. rm(letter_subject);
  69. rm(letter);
  70. #gc();
  72. #从替换过的序列injectedseqs中确定高质量区域的起始和结束位点
  73. last5endN <- which.isMatchingAt("N", injectedseqs, at=region5Len:1, follow.index=TRUE);#从region5Len开始向5’端找第一个"N"
  74. last5endN[is.na(last5endN)]=0;#没有"N"的序列会返回NA
  75. starts <- last5endN+1;
  76. first3endN <- which.isMatchingAt("N", injectedseqs, at=(raw_len-region3Len+1):raw_len, follow.index=TRUE);
  77. first3endN[is.na(first3endN)]=raw_len+1;
  78. ends <- first3endN-1;
  79. rm(last5endN);
  80. rm(first3endN);
  81. rm(injectedseqs);
  83. #注意从原始read中去掉去掉5'和3'端的N
  84. highQuaReads <- narrow(reads, start=starts, end=ends);#去掉去掉5'和3'端的N,得到start和end之间的部分（原始的）,即中间部分
  85. highSeqs <- narrow(seqs, start=starts, end=ends);
  86. rm(reads);
  87. rm(seqs);
  88. rm(starts);
  89. rm(ends);
  91. #根据每条highQuaReads中含N情况，决定留下谁去除谁
  92. nCount <- alphabetFrequency(highSeqs)[,"N"];#统计中间部分含有的"N",得到一个向量,对应每个read中含有的"N"的数量
  93. #根据中间部位"N"在所有reads中的分布,设定阈值,去除中间含有"N"过多的reads,并将剩下的reads两端的"N"去掉
  94. middleN <- nCount < nCutoff; #每个read根据他的nCount是否小于nCutoff来决定这条read是否保留
  95. highQuaReads <- highQuaReads[middleN]; #去掉低质量reads，只保留符合上面条件的高质量reads
  96. highQua_reads=highQua_reads+length(highQuaReads);#累计高质量reads的总数
  97. rm(highSeqs);
  98. rm(nCount);
  99. rm(middleN);
  101. ##################################
  102. ## trim 3' PCR2rc/adapter ##
  103. ##################################
  105. #去掉reads中含有的部分或整体PCR2rc/adapter
  106. max.mismatchs <- adapterMismatch*1:nchar(DNAString(PCR2rc));#特别要注意一定是PCR2的反向互补序列
  107. trimmedCoords <- trimLRPatterns(Rpattern = PCR2rc, subject = sread(highQuaReads), max.Rmismatch= max.mismatchs, with.Rindels=T,ranges=T);#这里得到坐标
  109. #先提取并保存剩下的3'端序列,仅供测试和检查使用
  110. #trimmed3End <- narrow(highQuaReads, start=end(trimmedCoords)+1, end=width(highQuaReads))#把trimm掉的那部分序列保留，以备人工检查
  111. #trimmed3End <- trimmed3End[!width(trimmed3End)==0]#去掉空数据
  112. #writeFastq(trimmed3End, file=trimmedFile, mode="a", full=FALSE);
  113. #rm(trimmed3End)
  115. #再提取并保存trim掉3'端剩下的序列
  116. trimmedReads <- narrow(highQuaReads, start=start(trimmedCoords), end=end(trimmedCoords));#利用上一步得到的坐标，同时trim核苷酸序列和质量分数序列
  117. rm(highQuaReads);
  118. rm(trimmedCoords);
  120. #去掉长度不足一定长度的reads,这里保存的reads对应结果表格中的Trimmed_reads和Trimmed_length
  121. trimmedReads <- trimmedReads[width(trimmedReads)>=readLength];#去掉PCR2rc/adapter的reads有的过短，不再保留
  122. trimmed_reads=trimmed_reads+length(trimmedReads);#累计trimmed reads的总数
  123. trimmed_len=trimmed_len+sum(width(trimmedReads));#累计trimmed reads的总长度
  124. writeFastq(trimmedReads, file=outfile, mode="a", compress = FALSE, full=FALSE);
  125. rm(trimmedReads);
  126. #gc();
  128. }#End yield while;
  129. close(inFh);
  130. trimmed_len=trimmed_len/trimmed_reads;#得到trimmed reads的平均长度
  131. lineofresult <- c(outfile, total_nCount, raw_reads, raw_len, highQua_reads, trimmed_reads, trimmed_len);#收集4列信息,样本文件名称、原始数据中"N"总数、reads长度和reads总数
  132. write(lineofresult,file = "trimmed.report", ncolumns =7,append = T, sep = "\t");#写入全部8列数据
  133. #删除全部参数
  134. rm(qualityCutoff);
  135. rm(region5Len);
  136. rm(region3Len);
  137. rm(nCutoff);
  138. rm(max.mismatchs);
  139. rm(trimmedFile);
  140. rm(lineofresult);
  141. print(paste("Finished processing file:", fastqfile));#屏幕输出,该文件处理完毕
  142. #gc();
  143. }
  144. removePatterns <- function(fastqfile, outfile, patterns, RdPerYield)
  145. {
  146. #这步实际上去除adapter的self-ligation等污染
  147. inFh <- FastqStreamer(fastqfile, n=RdPerYield);
  148. if (file.exists(outfile) ) {file.remove(outfile);} #如果输出文件已经存在必须删除，防止追加写
  149. cleaned_reads=0;
  150. cleaned_len=0;
  151. while (length(reads <- yield(inFh))) { #每次控制读入5百万个reads
  152. for(i in 1:dim(patterns)[1])#每个pattern都要在所有reads中搜索一遍,因此需要严格控制pattern数量
  153. {
  154. currentPattern=as.character(unlist(patterns))[i];#先转成字符串向量,再提取本次循环需要处理的字符串
  155. max.mismatchs <- as.integer(nchar(currentPattern)*0.1);#先得到字符数量,再得到错配总量,允许出错率为10%,这种模式比较严格
  156. result <- vcountPattern(currentPattern, sread(reads), max.mismatch= max.mismatchs, min.mismatch=0, with.indels=TRUE);#result是一个向量,每个数表示pattern在对应read中match的次数
  157. reads <- reads[result==0];#当前pattern没有命中的read保留
  158. }
  159. cleaned_reads=cleaned_reads+length(reads);#累计cleaned reads的总数
  160. cleaned_len=cleaned_len+sum(width(reads));#累计cleaned reads的总长度
  161. writeFastq(reads, file=outfile, mode="a", compress = FALSE, full=FALSE);#把pattern没有命中的read保存到sample.clean
  162. }#End yield while;
  163. close(inFh);
  164. cleaned_len=cleaned_len/cleaned_reads;#得到cleaned reads的平均长度
  165. lineofresult <- c(outfile,cleaned_reads,cleaned_len);#收集3列信息,样本文件名称、reads总数和reads平均长度
  166. write(lineofresult,file = "clean.report", ncolumns =3,append = T, sep = "\t");#写入3列数据
  167. print(paste("Finished processing file:", fastqfile));
  168. #gc();
  169. }

RNA-seq_clean.R at commit e5596fc, no license · at the source

Overview

Authors: Yang Yao1, Sha Zhou2,3, Zhi Cheng2, Shunmei Chen4, Yiyao Zhang2, Jingsong Shi5, Dongsheng Wei2, Tao Zhang2, Guangyou Duan6, Shan Gao2
ORCID iDs: Zhi Cheng, Shunmei Chen, Yiyao Zhang, Jingsong Shi, Dongsheng Wei, Tao Zhang, Guangyou Duan, Shan Gao
  1. Department of Neurology, Tianjin First Central Hospital, Nankai University, Tianjin 300192, China
  2. College of Life Sciences, Nankai University, Tianjin 300071, China
  3. State Key Laboratory of Medicinal Chemical Biology, College of Chemistry, Nankai University, Tianjin 300071, China
  4. Biomedical Engineering Research Institute, Kunming Medical University, Kunming 650500, China
  5. National Clinical Research Center for Kidney Diseases, Affiliated Jinling Hospital, Medical School, Nanjing University, Nanjing 210016, China
  6. School of Life Sciences, Qilu Normal University, Jinan 250200, China
Institutions: Nankai University (China); Kunming Medical University (China); Nanjing University (China); Qilu Normal University (China)
Journal: Cells, volume 15, issue 12, article 1086
Dates: received 25 May 2026; accepted 11 June 2026; published online 15 June 2026
Type: Research article · Language: English
License: CC BY
Identifiers: DOI 10.3390/cells15121086 · PMID 42346113 · PMCID PMC13297122 · OpenAlex W4408213249
Open access: gold, a free copy (OpenAlex)
Preprint: doi:10.32388/cnv1zb
Status: code verified
Categories: genetics / omics (modality), human (organism), mouse (organism), Alzheimer's / dementia (population), cellular / molecular (subfield)
Methods: Statistics
Keywords: alternative splicing, splice junction usage, transcriptome analysis, DGEA, APOE
MeSH: Alternative Splicing*, Alzheimer Disease*, Amyloid beta-Protein Precursor, Animals, Cell Differentiation, Humans, Induced Pluripotent Stem Cells, Mice, Microglia, Neurons, Protein Isoforms (* major topic)
Topic: RNA Research and Splicing (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: National Natural Science Foundation of China (32371153)
Citations: not cited yet (Europe PMC); 28 references in the paper
Research resources: H1 (WA01) hESCs RRID:CVCL_9771

Abstract

Accurately quantifying the expression of individual transcript isoforms remains a formidable challenge, especially in contexts such as neurodegenerative diseases and cancers, which are characterized by high isoform diversity. The present study introduces a junction-based method, named differential splicing frequency analysis (DSFA), which enables more sensitive detection of differential splicing using RNA-seq data. Unlike the existing exon-, isoform-, and event-based methods, DSFA quantifies splice junction usage. We applied DSFA to Alzheimer’s disease (AD)-associated genes through large-scale RNA-seq data mining. The present study is the first to establish that the APP770-, APP751-, APP695-, and APP752-encoding isoforms represent major isoforms of the APP gene. Three important findings are: (1) the APP752-encoding isoform exhibits immune cell specificity; (2) the relative proportion of the APP752-encoding isoform increases during the differentiation of induced pluripotent stem cells (iPSCs) into microglia, akin to the increase in relative proportion of the APP695-encoding isoform during iPSC differentiation into neurons; and (3) the APP751-encoding isoform predominates in both cancer and immune cells. Additionally, we identified APP/58417N and App/52804N as differentially expressed splice junctions in humans and mice, respectively. Through over-expression of U1 snRNA in human embryonic stem cell (hESC)-derived neurons, we found that U1 snRNA over-expression decreases the usage of APP/58417N in neurons, similar to the effects observed in AD samples. Our research highlights that the major isoforms of a gene can differ markedly in their expression across tissue and cell types.

Reproduced under the paper's license (CC BY), from the paper cited above.

Repository

Its files are read in the Code ↔ Paper reader above, with 2 matches between paragraphs and lines of code.

gaoshanT/Fastq_clean

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: e5596fc22f2e0db4415eba96049027d8207ed8b8, 2 February 2023
Languages: Perl (40), Python (5), R (3)
Size: 290 files, 48 scripts
Software Heritage: not archived
Found in: the text, “2.1. Data Acquisition and Analysis”
Holds: README, documentation
Not found: license file, CITATION.cff, environment file, tests, continuous integration
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
49 files
At the source: github.com/gaoshanT/Fastq_clean

Tracing map

Proposed by the machine: these links were found in the paper and verified at the source, without human review. The map will receive a Zenodo DOI once one of the paper's authors has validated it with their ORCID.

What the map holds:

  • 1 repository of the authors' code, each at its verified commit, with its license and how the link was found in the paper;
  • 48 scripts, each with its path and the digest of its content;
  • 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
  • neither the text of the paper nor the code itself.

Its JSON (tracing-map.json) is deposited on Zenodo with its DOI once the map is validated.

Data

Datasets cited

Data Availability Statement

The raw reads of four samples are openly available in the NCBI SRA database under the project accession number SRP178463. The gene expression matrix of SRP178463, generated according to Ensembl annotations release 114, is available in the NCBI GEO database under the series accession number GSE124951 (https://www.ncbi.nlm.nih.gov/geo/query/acc.cgi?acc=GSE124951). All other data used in the present study were obtained from public databases, and the identifiers (IDs) for all these data are provided in the Supplementary Files.

Reproduced under the paper's license (CC BY), from the paper cited above.

Versions

The history of this record: each version stored by the harvester or made by a correction of its authors or of the maintainers of its code, and what changed in its facts. The texts of the paper (its abstract, its availability statements) are not part of it; versions that changed only those are not listed.

Version 2, 28 September 2026

  • Authors: added Zhi Cheng (0000-0002-1341-5649); Shunmei Chen (0000-0001-5502-7794); Yiyao Zhang (0000-0003-3189-8450); Jingsong Shi (0000-0001-7250-8813); Dongsheng Wei (0000-0003-2670-6362); Guangyou Duan (0000-0002-6199-7882); removed Zhi Cheng; Shunmei Chen; Yiyao Zhang; Jingsong Shi; Dongsheng Wei; Guangyou Duan

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 10 authors, 5 keywords, 11 MeSH terms, 1 funder, 28 references, 1 RRID.

Cite

This paper

Yao, Y., Zhou, S., Cheng, Z., Chen, S., Zhang, Y., Shi, J., Wei, D., Zhang, T., Duan, G., & Gao, S. (2026). Investigating Alzheimer's Disease-Associated Genes Using Differential Splicing Frequency Analysis. Cells, 15(12), 1086. https://doi.org/10.3390/cells15121086

BibTeX

@article{yao2026investigating,
author = {Yao, Yang and Zhou, Sha and Cheng, Zhi and Chen, Shunmei and Zhang, Yiyao and Shi, Jingsong and Wei, Dongsheng and Zhang, Tao and Duan, Guangyou and Gao, Shan},
title = {{Investigating Alzheimer's Disease-Associated Genes Using Differential Splicing Frequency Analysis}},
journal = {Cells},
year = {2026},
month = jun,
volume = {15},
number = {12},
pages = {1086},
publisher = {Multidisciplinary Digital Publishing Institute (MDPI)},
issn = {2073-4409},
doi = {10.3390/cells15121086},
url = {https://doi.org/10.3390/cells15121086},
pmid = {42346113},
pmcid = {PMC13297122}
}

RIS

TY - JOUR
AU - Yao, Yang
AU - Zhou, Sha
AU - Cheng, Zhi
AU - Chen, Shunmei
AU - Zhang, Yiyao
AU - Shi, Jingsong
AU - Wei, Dongsheng
AU - Zhang, Tao
AU - Duan, Guangyou
AU - Gao, Shan
TI - Investigating Alzheimer's Disease-Associated Genes Using Differential Splicing Frequency Analysis
T2 - Cells
J2 - Cells
PY - 2026
DA - 2026/06/15
VL - 15
IS - 12
SP - 1086
SN - 2073-4409
PB - Multidisciplinary Digital Publishing Institute (MDPI)
DO - 10.3390/cells15121086
UR - https://doi.org/10.3390/cells15121086
LA - en
ER -

CSL-JSON

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"id": "10.3390/cells15121086",
"type": "article-journal",
"title": "Investigating Alzheimer's Disease-Associated Genes Using Differential Splicing Frequency Analysis",
"container-title": "Cells",
"author": [
{
"family": "Yao",
"given": "Yang"
},
{
"family": "Zhou",
"given": "Sha"
},
{
"family": "Cheng",
"given": "Zhi"
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{
"family": "Chen",
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{
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{
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"given": "Guangyou"
},
{
"family": "Gao",
"given": "Shan"
}
],
"container-title-short": "Cells",
"volume": "15",
"issue": "12",
"page": "1086",
"DOI": "10.3390/cells15121086",
"PMID": "42346113",
"PMCID": "PMC13297122",
"ISSN": "2073-4409",
"publisher": "Multidisciplinary Digital Publishing Institute (MDPI)",
"URL": "https://doi.org/10.3390/cells15121086",
"language": "en",
"issued": {
"date-parts": [
[
2026,
6,
15
]
]
}
}

The tracing map gets a citation of its own once an author has validated it and it has a DOI.

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[10] doi:10.1096/fj.202504243r
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