RNA-SEQ
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RNA-Seq is a method for transcriptome profiling that uses next generation sequencing technologies. RNA-Seq provides a comprehensive, quantitative, and unbiased view of RNA sequences within every sample, and is the most powerful tool currently available for analyzing gene expression.
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Microarray technology utilizes a pre-defined set of probes to capture and quantify specific RNA sequences. This means that microarrays are capable of detecting only a pre-selected set of transcripts. RNA-Seq relies on next generation sequencing technologies, enabling the identification and quantification of any RNA sequences in the sample, including novel sequences.
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The number of reads required depends upon the genome size, the number of known genes, and transcripts. Generally, we recommend 10 million reads for small genomes (i.e. bacteria) and 30 million reads for large genomes (i.e. human, mouse). Medium genomes often depend on the project, but we would generally recommend between 20-30 million reads per sample. For de novo transcriptome assembly projects, we recommend 100 million reads per sample.
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Single cell RNA-Seq provide transcriptional profiling of thousands of individual cells. This level of throughput analysis allows researchers to understand at the single-cell level what genes are expressed, in what quantities, and how they differ across thousands of cells within a heterogeneous sample(s).

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Standard RNA-Seq produces a representative snapshot of the transcriptional state averaged across all cells. The caveat with traditional RNA-Seq is the resolution of individual cells and cellular subpopulations are lost. Single-cell RNA-Seq allows researchers to not only identify cellular subpopulations, but to fully interrogate them at the single-cell level within a heterogeneous sample(s).

A:

We use 10X Genomics ChromiumTM controller coupled with sequencing on Illumina HiSeq. Chromium controller use microfluidic chip-based technology that partitions cells across tens of thousands of Gel Bead-In-Emulsions (GEMs).

This combination allows researchers to analyze thousands of single cells in a high-throughput fashion. Single Cell libraries are typically run using paired-end sequencing with dual indexing with the following configuration.

Read1: 26 cycles; i7 Index: 8 cycles; i5 Index: 0 cycles and Read2: 98 cycles

A:

We use 10X Genomics ChromiumTM controller coupled with sequencing on Illumina HiSeq. Chromium controller use microfluidic chip-based technology that partitions cells across tens of thousands of Gel Bead-In-Emulsions (GEMs).

This combination allows researchers to analyze thousands of single cells in a high-throughput fashion. Single Cell libraries are typically run using paired-end sequencing with dual indexing with the following configuration.

Read1: 26 cycles; i7 Index: 8 cycles; i5 Index: 0 cycles and Read2: 98 cycles