Dataset Information

Adapting the Smart-seq2 Protocol for Robust Single Worm RNA-seq.

ABSTRACT: Most nematodes are small worms that lack enough RNA for regular RNA-seq protocols without pooling hundred to thousand of individuals. We have adapted the Smart-seq2 protocol in order to sequence the transcriptome of an individual worm. While developed for individual Steinernema carpocapsae and Caenorhabditis elegans larvae as well as embryos, the protocol should be adaptable for other nematode species and small invertebrates. In addition, we describe how to analyze the RNA-seq results using the Galaxy online environment. We expect that this method will be useful for the studying gene expression variances of individual nematodes in wild type and mutant backgrounds.

SUBMITTER: Serra L

PROVIDER: S-EPMC5857950 | biostudies-literature | 2018 Feb

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Adapting the Smart-seq2 Protocol for Robust Single Worm RNA-seq.

Serra Lorrayne L Chang Dennis Z DZ Macchietto Marissa M Williams Katherine K Murad Rabi R Lu Dihong D Dillman Adler R AR Mortazavi Ali A

Bio-protocol 20180201 4

Most nematodes are small worms that lack enough RNA for regular RNA-seq protocols without pooling hundred to thousand of individuals. We have adapted the Smart-seq2 protocol in order to sequence the transcriptome of an individual worm. While developed for individual Steinernema carpocapsae and Caenorhabditis elegans larvae as well as embryos, the protocol should be adaptable for other nematode species and small invertebrates. In addition, we describe how to analyze the RNA-s ...[more]

PMID: 29564372

Similar Datasets

Project description:MotivationCell-type annotation of single-cell RNA-sequencing (scRNA-seq) data is a hallmark of biomedical research and clinical application. Current annotation tools usually assume the simultaneous acquisition of well-annotated data, but without the ability to expand knowledge from new data. Yet, such tools are inconsistent with the continuous emergence of scRNA-seq data, calling for a continuous cell-type annotation model. In addition, by their powerful ability of information integration and model interpretability, transformer-based pre-trained language models have led to breakthroughs in single-cell biology research. Therefore, the systematic combining of continual learning and pre-trained language models for cell-type annotation tasks is inevitable.ResultsWe herein propose a universal cell-type annotation tool, called CANAL, that continuously fine-tunes a pre-trained language model trained on a large amount of unlabeled scRNA-seq data, as new well-labeled data emerges. CANAL essentially alleviates the dilemma of catastrophic forgetting, both in terms of model inputs and outputs. For model inputs, we introduce an experience replay schema that repeatedly reviews previous vital examples in current training stages. This is achieved through a dynamic example bank with a fixed buffer size. The example bank is class-balanced and proficient in retaining cell-type-specific information, particularly facilitating the consolidation of patterns associated with rare cell types. For model outputs, we utilize representation knowledge distillation to regularize the divergence between previous and current models, resulting in the preservation of knowledge learned from past training stages. Moreover, our universal annotation framework considers the inclusion of new cell types throughout the fine-tuning and testing stages. We can continuously expand the cell-type annotation library by absorbing new cell types from newly arrived, well-annotated training datasets, as well as automatically identify novel cells in unlabeled datasets. Comprehensive experiments with data streams under various biological scenarios demonstrate the versatility and high model interpretability of CANAL.AvailabilityAn implementation of CANAL is available from https://github.com/aster-ww/CANAL-torch.Contactdengmh@pku.edu.cn.Supplementary informationSupplementary data are available at Journal Name online.

Dataset Information

Adapting the Smart-seq2 Protocol for Robust Single Worm RNA-seq.

Publications

Adapting the Smart-seq2 Protocol for Robust Single Worm RNA-seq.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets