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SMART-Seq® Total RNA Pico Input (ZapR® Mammalian)
SMART-Seq® Total RNA Pico Input with UMIs (ZapR® Mammalian) is designed for efficient preparation of Illumina sequencing libraries from picogram input amounts of total RNA. Proprietary SMART (Switching Mechanism at the 5′ end of RNA Template) technology, along with ZapR® rRNA depletion technology, enable processing of high-quality, partially degraded, or low-quality RNA, including RNA prepared from FFPE, LCM, or cell-free RNA samples. The kit features a streamlined workflow that retains strand information and incorporates an 8 nucleotide (nt) unique molecular identifier (UMI) during the reverse-transcription step that mitigates potential PCR errors and bias, resulting in improved transcript quantification and variant detection. Indexes and adapters for sequencing are incorporated during PCR amplification steps. The entire protocol, including ribodepletion, library prep, and purification, takes only ~6.5 hours.
Libraries produced with this kit achieve a high percentage of clusters passing filter (%PF) without significant additions of PhiX on both patterned and non-patterned Illumina sequencers such as MiSeq®, NextSeq®, and NovaSeq™. This, in turn, reduces sequencing costs by yielding more biologically relevant sequencing reads per run. Data from the kit tends to identify more unique transcripts with fewer reads mapping to rRNA and mtRNA and lower duplicate rates.
Overview
• Sequence coding and non-coding RNA biotypes with comprehensive data
• Use 250 pg–10 ng of total RNA from human, mouse, or rat as input
• Generate reproducible data from RNA of any quality, including FFPE, LCM, and cell-free RNA samples
• Identify more transcripts with fewer duplicates, including low-abundance transcripts
• Retain transcript strand-of-origin information with very high accuracy
• Go from start to finish in ~6.5h with an integrated workflow for library prep and/or with rRNA depletion
• Remove PCR errors and biases with seamless integration of unique molecular identifiers (UMIs)
• Mitigate index-hopping errors for improved data accuracy with unique dual indexes (UDIs)
