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Blood-based circular RNAs for early diagnosis of Alzheimer's disease.

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  1. [1] § Methods › Linear transcript identification ↔ linear_pipeline_circ_alignment/src/linear_RNA_pipeline.py, lines 318–325 · score 0.82 · Picard Collect RNA, Collect Alignment, Summary Metrics, linear RNA, FastQC, seq
  2. [2] § Methods › Linear transcript identification ↔ linear_pipeline_circ_alignment/src/run_Picard_QC.py, lines 24–36 · score 0.68 · Collect Alignment Summary, Picard Collect, Metrics, sequence, QC, linear

Paper

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

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The authors' code

Python · 353 lines · 18 KB · no license · 1 match

  1. #!/usr/bin/env python3
  2. import argparse
  3. import logging
  4. import sys
  5. import shutil
  6. import re
  8. sys.path.append("/src")
  9. from fastq_to_ubam import *
  10. from run_STAR_js import align_STAR_normal, cram_samtools, index_samtools, sort_samtools, cram_samtools
  11. from run_Picard_QC import picard_collect_RNA_metrics, picard_collect_alignment_metrics, picard_mark_dups
  12. from run_Salmon import salmon_quant
  13. from run_tin import calc_tin
  14. from run_fastqc import fastqc_fastq_PE, fastqc_SE
  15. ################################################################################
  16. # Setup
  17. ################################################################################
  19. # Argparse to get all the input
  20. parser = argparse.ArgumentParser(description='Run linear RNAseq pipeline')
  21. # to do maybe make this one argument that sometimes needs two values
  22. parser.add_argument('-r1', '--raw_input', help='Path to raw data (Read 1 for PE data)', required = True)
  23. parser.add_argument('-r2', '--input_read_2', help = 'Path to raw data read 2 file for PE data')
  24. parser.add_argument('--sample', help = 'Sample name', required = True)
  25. # TODO maybe add a crammed option?
  26. parser.add_argument('--file_type', help = 'Input file type for -r1 and -r2', choices = ["ubam", "fastq", "bam"], required = True)
  27. parser.add_argument('--tmp_dir', help ='Path to directory for temporary files', required = True)
  28. parser.add_argument('--read_type', help = 'Paired end or Single end reads', choices = ['PE', 'SE'], required = True)
  29. parser.add_argument('--stranded', help = 'Include for strand specific libraries', default = "NONE", choices = ["NONE", "FIRST_READ_TRANSCRIPTION_STRAND", "SECOND_READ_TRANSCRIPTION_STRAND"])
  30. parser.add_argument('--cohort', help = 'Full RNAseq cohort name', required = True )
  31. parser.add_argument('--tissue', help = 'Sample tissue type for hydra dir path', choices = ['brain', 'blood_pax', 'ipsc', 'plasma', 'csf'])
  32. parser.add_argument('--STAR_index', help = "Path to the directory with the STAR genome index refernce", required = True)
  33. parser.add_argument('--ref_flat', help = "Path to reference annotation in flat format", required = True)
  34. parser.add_argument('--annotation', help = "Path to annotaiton file for Salmon", required = True)
  35. parser.add_argument('--annote_bed', help = "Path to annotation in bed format for TIN", required = True)
  36. parser.add_argument('--rib_int', help = "Ribosomal (rRNA) interval list", required = True)
  37. parser.add_argument('--transcripts', help = "Path to Salmon transcripts index", required = True)
  38. parser.add_argument('--adapter_seq', default = 'null', help ="Adapter sequence for picard")
  39. parser.add_argument('--out_dir', help = 'Directory for output', required = True)
  40. parser.add_argument('--out_struct', help = "Directory structure of output", default = 'none', choices = ['none', 'hydra'])
  41. parser.add_argument( '-d', '--delete',\
  42. nargs = '*',\
  43. choices = ["input", "aligned_bam", "aligned_sorted_bam", "bais", "STAR_extras", "ubam", "aligned_transcriptome_bam", "md_bam"],\
  44. help = 'Include to delete one or more following files: input files, .Aligned.out.bam, Aligned.sortedByCoord.out.bam, all .bai files,\
  45. ._STARgenome and ._STARpass1, _unmapped.bam, .Aligned.toTranscriptome.out.bam, and .Aligned.sortedByCoord.out.md.bam respectively')
  46. parser.add_argument('-c', '--cram', action = 'store_true', help = 'Cram the following bams: Aligned.sortedByCoord.out.md.bam')
  47. parser.add_argument('--ref', help = 'Path to reference genome fasta (for cramming)', required= True) # TODO change this to be required only when cramming
  48. parser.add_argument('--input_to_merge', help = 'Path to secondary input file to concatenate with primary input (ie MSBB which is separated into aligned and unaligend reads)')
  49. parser.add_argument('--merge_file_type', help = 'Input file type for --input_to_merge', choices = ["ubam", "fastq", "bam"], default = "fastq")
  50. args = parser.parse_args()
  53. if args.read_type == 'PE' and args.file_type == 'fastq' and args.input_read_2 is None:
  54. parser.error("--input_read_2 required if input is fastq and read type is PE")
  56. if args.out_struct == 'hydra' and args.tissue is None:
  57. parser.error("--tissue required if output structure is hydra")
  61. # Maybe make this an error later
  62. if args.raw_input == args.input_read_2:
  63. parser.error('--input_read_2 and --raw_input cannot be the same file. Please check input.')
  67. # set up logs
  68. log_file_name = args.cohort + '_linear_pipeline.log'
  69. log_path = os.path.join(args.out_dir, log_file_name)
  70. # Create logger
  71. linear_logs = logging.getLogger('linear_RNA_pipeline')
  72. linear_logs.setLevel(logging.DEBUG)
  74. # Handler 1: file for all info+ logs
  75. log_file = logging.FileHandler(log_path)
  76. file_format = logging.Formatter("%(asctime)s:%(levelname)s:%(message)s")
  77. log_file.setLevel(logging.INFO)
  78. log_file.setFormatter(file_format)
  80. # Handler 2: stream for all warning +
  81. stream = logging.StreamHandler()
  82. streamformat = logging.Formatter("%(levelname)s:%(module)s:%(message)s")
  83. stream.setLevel(logging.WARNING)
  84. stream.setFormatter(streamformat)
  86. # Add handlers to logs
  87. linear_logs.addHandler(log_file)
  88. linear_logs.addHandler(stream)
  91. ################################################################################
  92. # Pipeline Steps
  93. ################################################################################
  94. def check_refs_exist(star_ref, flat_ref, annot_ref, annot_bed_ref, rib_int_ref, transcript_ref):
  95. star_exists = os.path.exists(star_ref)
  96. flat_exists = os.path.exists(flat_ref)
  97. annot_exists = os.path.exists(annot_ref)
  98. annot_bed_exists = os.path.exists(annot_bed_ref)
  99. rib_int_exists = os.path.exists(rib_int_ref)
  100. transcript_exists = os.path.exists(transcript_ref)
  101. if not star_exists:
  102. sys.exit(f'STAR reference not found: {star_ref}, exiting')
  103. if not flat_exists:
  104. sys.exit(f'flat reference not found: {flat_ref}, exiting')
  105. if not annot_exists:
  106. sys.exit(f'Annotation not found: {annot_ref}, exiting')
  107. if not annot_bed_exists:
  108. sys.exit(f'Bed format annotation not found: {annot_bed_ref}, exiting')
  109. if not rib_int_exists:
  110. sys.exit(f'Ribosomal interval list not found: {rib_int_ref}, exiting')
  111. if not transcript_exists:
  112. sys.exit(f'Salmon transcripts index not found: {transcript_ref}, exiting')
  114. def create_out_dir(dir_to_create):
  115. already_exists = os.path.exists(dir_to_create)
  116. if not already_exists:
  117. os.makedirs(dir_to_create, exist_ok = True) #note this will make any dirs missing on the path
  119. def setup_output_dirs(output_struct, out_dir, cohort_name, tissue, sample_id):
  120. if output_struct == "hydra":
  121. if tissue == "brain":
  122. tissue_folder = "01-Brain"
  123. elif tissue == "blood_pax":
  124. tissue_folder = "02-Blood_PAXgene"
  125. elif tissue == "ipsc":
  126. tissue_folder = "03-iPSC"
  127. elif tissue == "plasma":
  128. tissue_folder = "04-Plasma"
  129. else:
  130. tissue_folder = "05-CSF"
  131. linear_logs.info(f'Setting up output structure to comply with Hydras dir structure and placing in {out_dir}')
  132. #If outstructure = hydra create paths for :
  133. # 02-Processed/<tissue>/<cohort>/01-FastQC/${SAMPLEID} -- fastqc.htlm, fastqc.zip, picard qc
  134. fastqc_out_dir = os.path.join(out_dir, '02-Processed/02-GRCh38/', tissue_folder, cohort_name, '01-FastQC', sample_id)
  135. create_out_dir(fastqc_out_dir)
  136. # 02-Processed/<tissue>/<cohort>/02-Linear_TIN/${SAMPLEID} -- .bam, .bai, .tin.csv, .tin.xls, salmon
  137. linear_tin_processed_dir = os.path.join(out_dir, '02-Processed/02-GRCh38/', tissue_folder, cohort_name, '02-Linear_TIN', sample_id)
  138. create_out_dir(linear_tin_processed_dir)
  140. output_dirs = {"fastqc": fastqc_out_dir, "linear_tin": linear_tin_processed_dir}
  141. else:
  142. print(f'Placing all output in dir: {out_dir}')
  143. output_dirs = {"fastqc": out_dir, "linear_tin": out_dir }
  144. return output_dirs
  146. def convert_to_ubam(out_dir, sample_name, file_type, read_type, raw_input, input_read_2, tmp_dir, rg_name = 'A'):
  147. ubam_out = os.path.join(out_dir, f"{sample_name}_unmapped.bam")
  148. if file_type == 'fastq':
  149. print('Converting fastq to ubam')
  150. if read_type == 'PE':
  151. print('Using ' + raw_input + ' and ' + input_read_2 +' as input for ummaped bam')
  152. fastq_to_ubam_PE(raw_input, input_read_2, ubam_out, sample_name, rg_name)
  153. star_input = ubam_out
  154. else:
  155. print('Converting single fq to unmapped bam')
  156. fastq_to_ubam_SE(raw_input, ubam_out, sample_name, rg_name)
  157. star_input = ubam_out
  159. elif args.file_type == 'bam':
  160. bam_to_ubam(raw_input, ubam_out, tmp_dir, by_readgroup = 'false'),
  161. star_input = ubam_out
  162. else:
  163. print('Input data already ubam, proceeding to alignment')
  164. star_input = raw_input
  165. return star_input
  167. def align_with_star(star_input, sample_name, read_type, STAR_index, out_dir, tmp_dir):
  168. out_prefix = os.path.join(out_dir, f'{sample_name}.')
  169. print('Aligning ubam with STAR')
  170. STAR_file_input = "SAM "+ read_type
  171. align_STAR_normal( star_input, out_prefix, STAR_file_input, STAR_index, tmp_dir)
  172. aligned_bam_out = f"{out_prefix}Aligned.out.bam"
  173. aligned_transcript_bam = f"{out_prefix}Aligned.toTranscriptome.out.bam"
  174. return (aligned_bam_out, aligned_transcript_bam)
  176. def sort(out_dir, sample_name, aligned_bam_in):
  177. print('Sorting with samtools')
  178. out_prefix = os.path.join(out_dir, sample_name)
  179. sort_samtools(aligned_bam_in, out_prefix)
  180. sorted_bam_out = f"{out_prefix}.Aligned.sortedByCoord.out.bam"
  181. return sorted_bam_out
  183. def index(sorted_bam_out):
  184. print('Indexing with samools')
  185. index_samtools(sorted_bam_out)
  186. indexed_bam_out = f"{sorted_bam_out}.bai"
  187. return indexed_bam_out
  189. def fastqc(out_dir, sample_name, input_file_type, input_read_type, raw_input, raw_input2):
  190. if input_file_type == 'fastq' and input_read_type == 'PE':
  191. fastqc_fastq_PE(raw_input, raw_input2, sample_name, out_dir)
  192. else:
  193. fastqc_SE(raw_input, sample_name, out_dir)
  195. def delete_extras(delete, sample_name, aligned_bam, sorted_bam, indexed_bam, STAR_dir, indexed_md_bam, merged_ubam, input_2, transcriptome_bam, md_bam):
  196. if delete is not None:
  197. print("Deleting files specified with --delete option")
  198. if 'input' in args.delete:
  199. print('Deleting input file(s)')
  200. os.remove(args.raw_input)
  201. linear_logs.info(f'Deleting file: {args.raw_input}')
  202. if args.input_read_2 is not None:
  203. os.remove(args.input_read_2)
  204. linear_logs.info(f'Deleting file: {input_2}')
  205. if 'aligned_bam' in args.delete:
  206. print('Deleting aligned bam')
  207. os.remove(aligned_bam)
  208. linear_logs.info(f'Deleting file: {aligned_bam}')
  209. if 'md_bam' in args.delete and args.cram is not True: # added cram qualification -- if cramming is turned on then this file won't exist
  210. print('Deleting md bam')
  211. os.remove(md_bam)
  212. linear_logs.info(f'Deleting file: {md_bam}')
  213. if 'aligned_sorted_bam' in args.delete:
  214. print('Deleting STAR aligned & sorted bam')
  215. os.remove(sorted_bam)
  216. linear_logs.info(f'Deleting file: {sorted_bam}')
  217. if 'bais' in args.delete:
  218. print('Deleting bai files')
  219. os.remove(indexed_bam)
  220. os.remove(indexed_md_bam)
  221. linear_logs.info(f'Deleting file: {indexed_bam} and {indexed_md_bam}')
  222. if 'ubam' in args.delete:
  223. print('Deleting unmapped bam file')
  224. unmapped_bam = os.path.join(STAR_dir, f"{sample_name}_unmapped.bam")
  225. os.remove(unmapped_bam)
  226. linear_logs.info(f'Deleting file: {unmapped_bam}')
  227. # remove the input to merge if it exists
  228. if input_2 is not None:
  229. os.remove(merged_ubam)
  230. unmapped_bam_input2 = os.path.join(STAR_dir, f"{sample_name}_input2_unmapped.bam")
  231. os.remove(unmapped_bam_input2)
  232. if 'STAR_extras' in args.delete:
  233. print('Deleting ._STARgenome and ._STARpass1')
  234. STAR_pass1 = os.path.join(STAR_dir, f"{sample_name}._STARpass1")
  235. STAR_genome = os.path.join(STAR_dir,f"{sample_name}._STARgenome" )
  236. shutil.rmtree(STAR_pass1)
  237. shutil.rmtree(STAR_genome)
  238. linear_logs.info(f'Deleting files: {STAR_pass1} and {STAR_genome}')
  239. if 'aligned_transcriptome_bam' in args.delete:
  240. print('Deleting STAR aligned to transcriptome bam file')
  241. os.remove(transcriptome_bam)
  242. linear_logs.info(f'Deleting file: {transcriptome_bam}')
  244. def cram_bams(cram, mark_dups_bam, ref, sample_name, out_dir ):
  245. if cram is True:
  246. print(f"Cramming {mark_dups_bam_out}")
  247. out_prefix = os.path.join(out_dir, sample_name)
  248. md_cram_out = f"{out_prefix}.Aligned.sortedByCoord.out.md.cram"
  249. cram_samtools(mark_dups_bam, md_cram_out, ref )
  250. linear_logs.info(f"Cramming: {mark_dups_bam}")
  251. #then delete the orginal bam
  252. linear_logs.info(f"Deleting {mark_dups_bam}")
  253. os.remove(mark_dups_bam)
  255. def get_MSBB_read_group(sample_name):
  256. #MSBB read group ID is just the sample name UNLESS it has a third _ in name -- then it is everything before this underscore
  257. regex = re.compile('^[^_]+_[^_]+_[^_]+')
  258. read_group_id = regex.findall(sample_name)[0]
  259. return read_group_id
  263. # Note: currently only setup to work with SE reads -- for MSBB
  264. def merge_files(out_dir, sample_name, input_1_ubam, input_2, input_2_file_type, read_type, read_2, tmp_dir):
  265. if input_2 is not None:
  266. # fix sample names so they are different
  267. sample_name_input2 = f"{sample_name}_input2"
  268. # convert second file to ubam (first file should already be)
  269. #get the read group for MSBB -- TODO probably should make this check if this is MSBB
  270. msbb_read_group = get_MSBB_read_group(sample_name)
  271. input_2_ubam = convert_to_ubam(out_dir, sample_name_input2, input_2_file_type, read_type, input_2, read_2, tmp_dir, msbb_read_group)
  272. # concatenate with samtools
  273. unmapped_cat_bam = os.path.join(out_dir, f"{sample_name}_input1_input2_cat.bam")
  274. concat_ubams(input_1_ubam, input_2_ubam, unmapped_cat_bam)
  275. return unmapped_cat_bam
  276. else:
  277. return input_1_ubam
  282. # 0.1 check all references exist (so that don't have to quit half way thorugh):
  283. check_refs_exist(args.STAR_index, args.ref_flat, args.annotation, args.annote_bed, args.rib_int, args.transcripts)
  285. # 0.2 Set up output paths -- have an option to have this automatically structure like hydra
  286. out_dirs = setup_output_dirs(args.out_struct, args.out_dir, args.cohort, args.tissue, args.sample)
  287. print(f'''Output locations: \n fastqc: {out_dirs["fastqc"]} \n linear and tin processed: {out_dirs["linear_tin"]}
  288. salmon quant: {out_dirs["linear_tin"]} \n tin_summary: {out_dirs["linear_tin"]}''')
  290. # 0.3 set up tmp dir -- include JOB ID in path to prevent conflicts
  291. tmp_dir_path = os.path.join(args.tmp_dir, os.getenv('LSB_JOBID'))
  292. create_out_dir(tmp_dir_path)
  293. print(f'''tmp location: {tmp_dir_path}''')
  295. # 1. Run fastqc (if we don't need to merge files first)
  296. if args.input_to_merge is None:
  297. fastqc(out_dirs["fastqc"], args.sample, args.file_type, args.read_type, args.raw_input, args.input_read_2)
  299. # 2. convert to Ubam (from fastq or aligned bam)
  300. ubam_out = convert_to_ubam(out_dirs["linear_tin"], args.sample, args.file_type, args.read_type, args.raw_input, args.input_read_2, tmp_dir_path)
  302. # 2.1 concatenate if needed
  303. merged_ubam_out = merge_files(out_dirs["linear_tin"], args.sample, ubam_out, args.input_to_merge, args.merge_file_type, args.read_type, args.input_read_2, tmp_dir_path)
  305. # 2.1.2 run fastqc on concatenated bams
  306. if args.input_to_merge is not None:
  307. fastqc(out_dirs["fastqc"], args.sample, "ubam", args.read_type, merged_ubam_out, args.input_read_2)
  309. # 3. Align with STAR
  310. aligned_bam_out, aligned_transcript_bam_out = align_with_star(merged_ubam_out, args.sample, args.read_type, args.STAR_index, out_dirs["linear_tin"], tmp_dir_path)
  312. # 4. samtools sort
  313. sorted_bam_out = sort(out_dirs["linear_tin"], args.sample, aligned_bam_out)
  315. # 5. samtools index
  316. indexed_bam_out = index(sorted_bam_out)
  318. # 6. Post Alignment Picard QC ( Collect RNAseq metrics, Collect Alignemt summary metrics, Mark dups)
  319. RNAseq_metrics_out = os.path.join(out_dirs["fastqc"], f"{args.sample}.RNA_Metrics.txt")
  320. picard_collect_RNA_metrics(sorted_bam_out, RNAseq_metrics_out, args.ref_flat, args.rib_int, tmp_dir_path, args.stranded)
  321. Alignment_metrics_out = os.path.join(out_dirs["fastqc"], f"{args.sample}.Summary_metrics.txt")
  322. picard_collect_alignment_metrics(sorted_bam_out, Alignment_metrics_out, tmp_dir_path, args.adapter_seq)
  323. mark_dups_bam_out = os.path.join(out_dirs["linear_tin"], f"{args.sample}.Aligned.sortedByCoord.out.md.bam")
  324. mark_dups_txt_out = os.path.join(out_dirs["fastqc"],f"{args.sample}.marked_dup_metrics.txt")
  325. picard_mark_dups(sorted_bam_out, mark_dups_bam_out, mark_dups_txt_out, tmp_dir_path )
  327. # 7. quantify with salmon
  328. salmon_out = os.path.join(out_dirs["linear_tin"],f'{args.sample}_salmon')
  329. salmon_quant(aligned_transcript_bam_out, salmon_out, args.annotation, args.transcripts)
  331. # 8. TIN
  332. # first index md bam
  333. indexed_md_bam_out = index(mark_dups_bam_out)
  334. #then run tin.py
  335. calc_tin(mark_dups_bam_out, args.annote_bed)
  336. # move summary tin ouput
  337. tin_summary_current = f"{args.sample}.Aligned.sortedByCoord.out.md.summary.txt"
  338. tin_summary_new_loc = os.path.join(out_dirs["linear_tin"], f"{args.sample}.Aligned.sortedByCoord.out.md.summary.txt")
  339. tin_xls_current = f"{args.sample}.Aligned.sortedByCoord.out.md.tin.xls"
  340. tin_xls_new_loc = os.path.join(out_dirs["linear_tin"], f"{args.sample}.Aligned.sortedByCoord.out.md.tin.xls")
  341. shutil.move(tin_summary_current, tin_summary_new_loc)
  342. shutil.move(tin_xls_current, tin_xls_new_loc)
  344. # cram bams
  345. cram_bams(args.cram, mark_dups_bam_out, args.ref, args.sample, out_dirs["linear_tin"])
  347. ## 9. Clean up
  348. # delete the intermediate files we don't normally keep -- as requested by user with --delete argument
  349. delete_extras(args.delete, args.sample, aligned_bam_out, sorted_bam_out, indexed_bam_out, out_dirs["linear_tin"], \
  350. indexed_md_bam_out, merged_ubam_out, args.input_to_merge, aligned_transcript_bam_out, mark_dups_bam_out)

linear_RNA_pipeline.py at commit c6fc549, no license · at the source

Overview

Authors: Bridget Phillips1,2, Jessie Sanford1,2, Vaibhav A Janve3,4, Menghan Liu1,2, Matt Johnson1,2, Katherine Gong1,2, Kristy Bergmann1,2, Joseph Lowery1,2, Allison Flynn1,2, William Brock1,2, Brenda Sanchez Montejo1,2, Nicholas Sykora1,2, John Budde1,2, Nikolaos Mellios5, Grigorios Papageorgiou5, Reisa A Sperling6, Rachel F Buckley6,7,8, Mabel Seto7, John C Morris9,10,11, Joel S Perlmutter9,12, Paul T Kotzbauer9, Richard J Perrin9,10,11, Timothy J Hohman3,4, Laura Ibanez1,2,9, Carlos Cruchaga1,2
ORCID iDs: Bridget Phillips, Jessie Sanford, Menghan Liu, Matt Johnson, Katherine Gong, Joseph Lowery, Nicholas Sykora, John Budde, Nikolaos Mellios, Grigorios Papageorgiou, Reisa A Sperling, Rachel F Buckley, Joel S Perlmutter, Richard J Perrin, Timothy J Hohman, Carlos Cruchaga
  1. Department of Psychiatry, Washington University School of Medicine, St. Louis, MO USA
  2. NeuroGenomics and Informatics Center, Washington University School of Medicine, St. Louis, MO USA
  3. Vanderbilt Memory and Alzheimer’s Center, Vanderbilt University Medical Center, Nashville, TN USA
  4. Department of Neurology, Vanderbilt University Medical Center, Nashville, TN USA
  5. Circular Genomics, San Diego, CA USA
  6. Department of Neurology, Massachusetts General Hospital, Harvard Medical School, Boston, MA USA
  7. Center for Alzheimer’s Research and Treatment, Brigham and Women’s Hospital, Harvard Medical School, Boston, MA USA
  8. School of Psychological Sciences, University of Melbourne, Melbourne, Victoria Australia
  9. Department of Neurology, Washington University School of Medicine, St. Louis, MO USA
  10. Department of Pathology and Immunology, Washington University School of Medicine, St. Louis, MO USA
  11. Knight Alzheimer Disease Research Center, Washington University School of Medicine, St. Louis, MO USA
  12. Department of Radiology, Washington University School of Medicine, St. Louis, MO USA
Institutions: Washington University in St. Louis (United States); Vanderbilt University Medical Center (United States); Harvard University (United States); Massachusetts General Hospital (United States); Brigham and Women's Hospital (United States); The University of Melbourne (Australia)
Journal: Nature medicine, volume 32, issue 8, pages 2857-2864
Dates: received 3 November 2025; accepted 27 May 2026; published online 1 July 2026; in print 2026
Type: Research article · Language: English
License: CC BY-NC-ND
Identifiers: DOI 10.1038/s41591-026-04485-5 · PMID 42387213 · PMCID PMC13472853 · OpenAlex W7166805726
Open access: hybrid, a free copy (OpenAlex)
Status: code verified
Categories: genetics / omics (modality), human (organism), Alzheimer's / dementia (population), clinical / translational (subfield)
Methods: Connectivity, Statistics, Smoothing, state filtering, decompositions, Machine learning, fMRI & imaging
Keywords: Alzheimer's disease, RNA sequencing, Predictive markers, Machine learning
MeSH: Alzheimer Disease*, RNA, Circular*, Aged, Aged, 80 and over, Amyloid beta-Peptides, Biomarkers, Early Diagnosis, Female, Humans, Male, tau Proteins (* major topic)
Topic: Circular RNAs in diseases (Molecular Biology, Biochemistry, Genetics and Molecular Biology), according to OpenAlex
Funding: NIA NIH HHS (R01 AG044546); U.S. Department of Health & Human Services | NIH | National Institute on Aging (U.S. National Institute on Aging) (R01AG044546); U.S. Department of Health &amp; Human Services | NIH | National Institute on Aging (R01AG044546)
Citations: not cited yet (Europe PMC); 41 references in the paper

Abstract

The abstract is not reproduced here: the paper's license (CC BY-NC-ND) does not allow it. Read it in the paper, at the publisher or on Europe PMC.

Repository

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neurogenomicsandinformatics/rnaseq_pipeline

License: none: the authors keep all their rights
State: the link answers, verified on 27 September 2026
Evidence: files inventoried
Commit: c6fc54934abcbc152f2df339a29fc07126cae036, 18 March 2026
Languages: Python (10)
Size: 16 files, 10 scripts
Software Heritage: not archived
Found in: “Code availability”
Holds: environment (circ_quant_DCC/Dockerfile, linear_pipeline_circ_alignment/Dockerfile.dockerfile, linear_pipeline_circ_alignment/src/gffread.dockerfile)
Not found: README, license file, CITATION.cff, tests, continuous integration, documentation
Tools: NumPy (1 file), pysam (1 file)
Availability: 1 check, the latest on 27 September 2026: the link answers
  • 27 September 2026: the link answers
10 files
At the source: github.com/NeuroGenomicsAndInformatics/RNAseq_pipeline/tree/multiple_fastq_merge/src

Code availability statement

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Tracing map

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What the map holds:

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  • 2 matches between paragraphs of the paper and lines of the code (method lexical-v1);
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Data

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Versions

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Version 2, 28 September 2026

  • Publisher: n/a → Nature Portfolio

Version 1, 27 September 2026: the first record

Recorded: type, language, journal, volume, issue, pages, dates, 25 authors, 4 keywords, 11 MeSH terms, 3 funders, 39 references.

Cite

This paper

Phillips, B., Sanford, J., Janve, V. A., Liu, M., Johnson, M., Gong, K., Bergmann, K., Lowery, J., Flynn, A., Brock, W., Montejo, B. S., Sykora, N., Budde, J., Mellios, N., Papageorgiou, G., Sperling, R. A., Buckley, R. F., Seto, M., Morris, J. C., . . . Cruchaga, C. (2026). Blood-based circular RNAs for early diagnosis of Alzheimer's disease. Nature medicine, 32(8), 2857-2864. https://doi.org/10.1038/s41591-026-04485-5

BibTeX

@article{phillips2026blood,
author = {Phillips, Bridget and Sanford, Jessie and Janve, Vaibhav A and Liu, Menghan and Johnson, Matt and Gong, Katherine and Bergmann, Kristy and Lowery, Joseph and Flynn, Allison and Brock, William and Montejo, Brenda Sanchez and Sykora, Nicholas and Budde, John and Mellios, Nikolaos and Papageorgiou, Grigorios and Sperling, Reisa A and Buckley, Rachel F and Seto, Mabel and Morris, John C and Perlmutter, Joel S and Kotzbauer, Paul T and Perrin, Richard J and Hohman, Timothy J and Ibanez, Laura and Cruchaga, Carlos},
title = {{Blood-based circular RNAs for early diagnosis of Alzheimer's disease}},
journal = {Nature medicine},
year = {2026},
month = jul,
volume = {32},
number = {8},
pages = {2857--2864},
publisher = {Nature Portfolio},
issn = {1078-8956},
doi = {10.1038/s41591-026-04485-5},
url = {https://doi.org/10.1038/s41591-026-04485-5},
pmid = {42387213},
pmcid = {PMC13472853}
}

RIS

TY - JOUR
AU - Phillips, Bridget
AU - Sanford, Jessie
AU - Janve, Vaibhav A
AU - Liu, Menghan
AU - Johnson, Matt
AU - Gong, Katherine
AU - Bergmann, Kristy
AU - Lowery, Joseph
AU - Flynn, Allison
AU - Brock, William
AU - Montejo, Brenda Sanchez
AU - Sykora, Nicholas
AU - Budde, John
AU - Mellios, Nikolaos
AU - Papageorgiou, Grigorios
AU - Sperling, Reisa A
AU - Buckley, Rachel F
AU - Seto, Mabel
AU - Morris, John C
AU - Perlmutter, Joel S
AU - Kotzbauer, Paul T
AU - Perrin, Richard J
AU - Hohman, Timothy J
AU - Ibanez, Laura
AU - Cruchaga, Carlos
TI - Blood-based circular RNAs for early diagnosis of Alzheimer's disease
T2 - Nature medicine
J2 - Nat Med
PY - 2026
DA - 2026/07/01
VL - 32
IS - 8
SP - 2857
EP - 2864
SN - 1078-8956
PB - Nature Portfolio
DO - 10.1038/s41591-026-04485-5
UR - https://doi.org/10.1038/s41591-026-04485-5
LA - en
ER -

CSL-JSON

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

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