diff --git a/Utilities/for_taxonomy/Query_SRA_egs.py b/Utilities/for_taxonomy/Query_SRA_egs.py
new file mode 100644
index 0000000..bc9deda
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+++ b/Utilities/for_taxonomy/Query_SRA_egs.py
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+'''
+Modified by Elinor 2/13 to grab recent assemblies (since 2020) and GCA codes. Input is folder 'unique_taxon_lists' with files
+of keywords by major clade (separated by new lines). Put -t (transcriptome, SRA db) or -g (genome, assembly db) in the command 
+line to specify data type
+
+restrictions:
+	All data is since 2020
+	SRA excludes entries with 'Amplicon' in the description, and outputs the experiment and sequencing technology.
+
+example command line: python Query_SRA_egs.py -t
+
+Output: file of species, IDs, and GCA or SRR codes AND a file with uniquified codes. Updated 3.21.23 to output sequencing machine and type of experiment from SRA
+
+'''
+
+from Bio import Entrez
+from Bio import SeqIO
+import os
+import sys
+
+
+def get_args():
+	
+	Entrez.email = "@smith.edu"#CHANGE UR EMAIL
+	Entrez.tool = "Biopython_NCBI_Entrez_downloads.ipynb"
+
+	if len(sys.argv) < 2:
+		print(f'enter -t or -g in command line to choose genomes (-g) or transcriptomes (-t)')
+	if '-t' in sys.argv:
+		data_type = False
+	elif '-g' in sys.argv:
+		data_type = True
+
+	with open('RecentIDs.csv', 'w') as o:#starts output file and writes header
+		o.write('major clade, keyword, species, ID, experiment, sequencing technology, GCA/SRR,\n')
+
+	get_keywords(data_type)
+
+
+def get_keywords(data_type):
+
+	for file in os.listdir('unique_taxon_lists'):
+		if file.endswith('_unique.csv'):#put name of file to look at here. or only .csv to look at all of them
+			with open(f'unique_taxon_lists/{file}', 'r') as lines:#read each file
+				mc = file.split("_unique.csv")[0]
+				print(f'Searching taxonomic names in {mc}\n\n')
+				
+				for line in lines.readlines():#iterate file
+					keyword = line.strip()#keyword for genbank search is each word in the files
+					
+					if data_type == False:
+						fetch_SRA(mc, keyword)
+					if data_type == True:
+						fetch_CDS(mc, keyword)
+
+	write_unique_codes()
+
+
+def fetch_CDS(mc, keyword):#searches your keywords in the assembly database
+	print(f'\nGrabbing recent CDSs')
+	all_stuff = []#initiate list, will put genbank codes into this
+
+	#get IDs of assemblies for keyword since 2020. Returns multiple IDs
+	handle = Entrez.esearch(db="assembly", term=keyword + "[Organism:exp]" + "2020 [SeqReleaseDate]:3000", retmax=100)
+	id_record = Entrez.read(handle)
+	print(f'There are {len(id_record["IdList"])} assemblies labeled as {keyword} in genbank since 2020\nFetching IDs and GCAs\n')
+
+	#Iterate through list of IDs given above, seach for their associated GCAs. Only one GCA for each ID, and each corresponds to 1 individual sequenced
+	for tax_id in id_record['IdList']:
+		handle = Entrez.esummary(db="assembly", id=tax_id, retmode="text")
+		gca_records = Entrez.read(handle, validate=False)
+		handle.close()
+		
+		#parse the output (its really awful. pythonic turduken: dict(list(str(dict))) type deal)
+		for record in gca_records['DocumentSummarySet']['DocumentSummary']:
+			sp = record['Organism']
+			gca=record['AssemblyAccession']
+			stuff = f'{mc}, {keyword}, {sp},{tax_id}, , ,{gca}'
+			all_stuff.append(stuff)
+	
+	write_to_csv(all_stuff)#send this new info to be added to output sheet
+
+
+def fetch_SRA(mc, keyword):#searches your keywords in the SRA db
+	
+	all_stuff = []
+	print(f'\nGrabbing recent SRRs')
+	# get IDs from taxonomies
+	handle = Entrez.esearch(db="sra", term=keyword + "[Organism:exp]"+ " 2020:2023[PDAT]", retmax=100)
+	id_record = Entrez.read(handle, validate = False)
+	print(f'There are {len(id_record["IdList"])} SRAs labeled as {keyword} in genbank since 2020\nFetching SRAs\n')
+
+
+	#get SRRs for taxonomy
+	for rec in id_record['IdList']:#iterates through all of the IDs for the taxonomy
+		tax_id = rec
+		handle = Entrez.esummary(db="sra", id=tax_id)
+		srr_records = Entrez.read(handle)#parse genbank info
+
+		#parse out all information needed from genbank info
+		sp = srr_records[0]['ExpXml'].split('ScientificName="')[1].split('"')[0]#extract species from genbank info
+		srr = srr_records[0]['Runs'].split('"')[1]#extract srr from genbank info
+		seq_type = srr_records[0]['ExpXml'].split('<LIBRARY_STRATEGY>')[1].split('</LIBRARY_STRATEGY>')[0]#parse to "library_strategy" parameter to check if its amplicon
+		machine = srr_records[0]['ExpXml'].split('<Platform instrument_model="')[1].split('">')[0]#get the type of sequencing machine used
+		if 'AMPLICON' not in seq_type:
+			stuff = f'{mc}, {keyword}, {sp}, {tax_id}, {seq_type}, {machine}, {srr},'#write to comma separated string
+			all_stuff.append(stuff)
+
+
+	write_to_csv(all_stuff)
+
+def write_to_csv(data):#writes the output from fetch_SRA or fetch_CDS to a csv
+	with open('RecentIDs.csv', 'a+') as o:
+		for i in data:
+			o.write(f'{i}\n')
+
+def write_unique_codes():#uniquify the list of IDs that the scipt grabbed. since we are searching all taxanomic levels, we query many repeats so this removes them.
+
+	#Writing unique files
+	with open('RecentIDs.csv', 'r') as o:# read file of all data
+		taxa = o.readlines()
+		print(f'\nThere are {len(taxa)} codes before uniquifying\n\n')
+		unique_lines = {line.split(', ')[-1] : line.split(', ')[0:-1] for line in taxa}#makes dictionary of SRR/GCA:other info to uniquify the codes
+		print(f'\nYou have {len(unique_lines)} unique codes... writing them to unique_taxa.csv')
+
+	with open ('unique_taxa.csv', 'w') as o:#start csv of unique codes
+		for gca, other in unique_lines.items():#parse uniquified dictionary
+			o.write(f'{(", ").join(other)}, {gca}')#write out (use join to convert the list containing other info to a string)
+
+
+
+if __name__ == '__main__':
+	get_args()