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IgSeqR: a protocol for the identification, assembly, and characterization of full-length tumor Immunoglobulin transcripts from unselected RNA sequencing data

IgSeqR: a protocol for the identification, assembly, and characterization of full-length tumor Immunoglobulin transcripts from unselected RNA sequencing data
IgSeqR: a protocol for the identification, assembly, and characterization of full-length tumor Immunoglobulin transcripts from unselected RNA sequencing data
Immunoglobulin (IG) gene analysis provides fundamental insight into B-cell receptor structure and function. In B-cell tumors, it can inform the cell of origin and clinical outcomes. Its clinical value has been established in the two types of chronic lymphocytic leukemia with unmutated or mutated IGHV genes and is emerging in other B-cell tumors. The traditional PCR-based techniques, which are labor-intensive, rely on the attainment of either a dominant sequence or a small number of subclonal sequences and do not allow automated matching with the clonal phenotypic features. Extraction of the expressed tumor IG transcripts using high-throughput RNA sequencing (RNA-seq) can be faster and allow the collection of multiple sequences matched with the transcriptome profile. Analytical tools are regularly sought to increase the accuracy, depth, and speed of acquisition of the full IGV-(IGD)-IGJ-IGC sequences and combine the IG characteristics with other RNA-seq data. We provide here a user-friendly protocol for the rapid extraction, identification, and accurate determination of the full (leader to constant region) tumor IG templated and non-templated transcript sequence from RNA-seq. The derived amino acid sequences can be interrogated for their physico-chemical characteristics and, in certain lymphomas, predict tumor glycan types occupying acquired N-glycosylation sites. These features will then be available for association studies with the tumor transcriptome. The resulting information can also help refine diagnosis, prognosis, and potential therapeutic targeting in the most common lymphomas.
bioRxiv
Bryant, Dean
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Sale, Benjamin
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Chiodin, Giorgia
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Tatterton, Dylan
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Stevens, Benjamin
f6fde136-3f94-40ad-b092-97dc37189d69
Adlaon, Alyssa
98c4be5d-6bba-4ef5-8bb6-05c69f0d4d50
Snook, Erin
3b64eebf-b74a-4196-a638-125c9f2e6862
Batchelor, James
e53c36c7-aa7f-4fae-8113-30bfbb9b36ee
Orfao, Alberto
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Forconi, Francesco
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Bryant, Dean
10ed83e8-8080-4d9c-bba5-df9d4eec3a10
Sale, Benjamin
9dead432-5956-4b59-9d10-9d9db8a10ba1
Chiodin, Giorgia
4b3e9525-b377-4d16-b69a-e05d2e7854fe
Tatterton, Dylan
ea972585-da97-46b7-bbac-7025ff42e294
Stevens, Benjamin
f6fde136-3f94-40ad-b092-97dc37189d69
Adlaon, Alyssa
98c4be5d-6bba-4ef5-8bb6-05c69f0d4d50
Snook, Erin
3b64eebf-b74a-4196-a638-125c9f2e6862
Batchelor, James
e53c36c7-aa7f-4fae-8113-30bfbb9b36ee
Orfao, Alberto
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Forconi, Francesco
ce9ed873-58cf-4876-bf3a-9ba1d163edc8

[Unknown type: UNSPECIFIED]

Record type: UNSPECIFIED

Abstract

Immunoglobulin (IG) gene analysis provides fundamental insight into B-cell receptor structure and function. In B-cell tumors, it can inform the cell of origin and clinical outcomes. Its clinical value has been established in the two types of chronic lymphocytic leukemia with unmutated or mutated IGHV genes and is emerging in other B-cell tumors. The traditional PCR-based techniques, which are labor-intensive, rely on the attainment of either a dominant sequence or a small number of subclonal sequences and do not allow automated matching with the clonal phenotypic features. Extraction of the expressed tumor IG transcripts using high-throughput RNA sequencing (RNA-seq) can be faster and allow the collection of multiple sequences matched with the transcriptome profile. Analytical tools are regularly sought to increase the accuracy, depth, and speed of acquisition of the full IGV-(IGD)-IGJ-IGC sequences and combine the IG characteristics with other RNA-seq data. We provide here a user-friendly protocol for the rapid extraction, identification, and accurate determination of the full (leader to constant region) tumor IG templated and non-templated transcript sequence from RNA-seq. The derived amino acid sequences can be interrogated for their physico-chemical characteristics and, in certain lymphomas, predict tumor glycan types occupying acquired N-glycosylation sites. These features will then be available for association studies with the tumor transcriptome. The resulting information can also help refine diagnosis, prognosis, and potential therapeutic targeting in the most common lymphomas.

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2024.09.03.611002v1.full - Author's Original
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Published date: 4 September 2024

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Local EPrints ID: 495765
URI: http://eprints.soton.ac.uk/id/eprint/495765
PURE UUID: a318b6f9-6eb1-474e-a273-083647907f07
ORCID for Dean Bryant: ORCID iD orcid.org/0000-0003-3163-608X
ORCID for Benjamin Sale: ORCID iD orcid.org/0000-0003-3292-1886
ORCID for Dylan Tatterton: ORCID iD orcid.org/0000-0002-1453-7496
ORCID for James Batchelor: ORCID iD orcid.org/0000-0002-5307-552X
ORCID for Francesco Forconi: ORCID iD orcid.org/0000-0002-2211-1831

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Date deposited: 21 Nov 2024 17:53
Last modified: 22 Nov 2024 03:00

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Contributors

Author: Dean Bryant ORCID iD
Author: Benjamin Sale ORCID iD
Author: Giorgia Chiodin
Author: Dylan Tatterton ORCID iD
Author: Benjamin Stevens
Author: Alyssa Adlaon
Author: Erin Snook
Author: James Batchelor ORCID iD
Author: Alberto Orfao

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