Optional scNetViz lab Module: Single Cell RNASeq Analysis with scNetViz | Analysis of routes and networks of omic data (June 2023) (2023)

FAQs

What is pathway analysis for scRNA-seq? ›

The pathway analysis is at the very end of a scRNA-seq workflow. This means, that any Q/C was already performed, the data was normalized and cells were already clustered. The ReactomeGSA package can now be used to get pathway-level expression values for every cell cluster.

Each run takes 16-36hrs depending on the type of sequencing being done. Putting all these steps together and allowing for some amount of troubleshooting and scheduling around other runs, we typically take 1-2wks to get from start to finish.

Bulk RNA-seq is typically used to assess changes due to experimental conditions, whereas scRNA-seq is more frequently used to assess differences between cell types (or to find cell types).

Single-Cell RNA-Seq requires at least 50,000 cells (1 million is recommended) as an input. See below for more information about sample submission guidelines.

Limitations of scRNA-seq in Cardiac Tissue

Both contribute to a higher difficulty in detecting the low abundant transcripts. The low amount of transcripts often resulted from library preparation leads to high levels of computational noise, which disturbs data analysis and may mask underlying biological variation.

Sequencing costs are approximately $125/sample for single-end 50bp sequencing and $250/sample for paired-end 100bp/150bp sequencing.

Sequencing RNA is much more difficult than mapping DNA, with hundreds of millions of reads (of about 100 bp each), interrupted transcripts, and more obstacles to achieve alignments.

Read Alignment

Mapping RNA-Seq reads to the genome is considerably more challenging than mapping DNA sequencing reads because many reads map across splice junctions.

ChIP-Seq may require only a few reads (~5-15 million) for a highly targeted transcription factor, and many more reads (~50 million) for a ubiquitous protein such as a histone mark pull-down.

Single-cell RNA sequencing helps in exploring the complex systems beyond the different cell types. It enables cell-by-cell molecular as well as cellular characterization of the cells. The scRNA-Seq makes it possible to explore complex systems such as the immune system without any limitation.

What is the difference between stranded and non-stranded RNA-Seq? ›

The read ambiguity in stranded RNA-seq arises only from overlapping genes transcribed from the same strand. In contrast, for non-stranded RNA-seq, the ambiguity arises from both the overlapping genes on the same strand and also from the opposite strands.

RNA sequencing generated much more bacterial reads than DNA sequencing and has the advantages of detecting actively transcribed infections, enabling differential gene expression analysis and the possibility to detect RNA genomes.

scRNA seq using the 10x Genomics system

$6600 for 4 samples (can handle 4 at a time on the 10X machine) we typically plan to capture 1000 cells per sample with library prep through the BU core.

So what depth is sufficient? One study showed that estimated expression levels from one million reads per cell strongly correlate with those from 10 million reads per cell⁴, suggesting that one million reads per cell may suffice.

RNA Isolation

We require a minimum of 500 ng of total RNA for QC and library preparation for Illumina sequencing. A number of well-established commercial kits and protocols exist for a variety of species and tissue/cell types.

The primary limitation in scRNA-seq technology is high dropout noise level caused by the poor sensitivity of scRNA-seq technology, which makes low-expression genes hard to detect. scImpute and SAVER are existing tools for solving the noise in scRNA-seq data.

In 5' scRNA-Sequencing, poly(A)+ mRNAs are reverse transcribed using a polydT primer. However, in contrast to 3' scRNA-Sequencing, the sequencing barcodes are not adjacent to the polydT primer, but they are located at the 5' end of the transcripts.

Disadvantages of illumina sequencing

One of the main drawbacks of the Illumina/Solexa platform is the high requirement for sample loading control because overloading can result in overlapping clusters and poor sequencing quality which results the overall error rate of this sequencing technology is about 1% [22,23].

Nonetheless, RNA sequencing is still expensive and time-consuming, because it first requires the costly preparation of an entire genomic library -- the DNA pool generated from the RNA of cells -- while the data itself are also difficult to analyze.

Is RNA-seq cheaper than microarray? ›

In most cases, obtaining the expression profile of your sample would still be a bit cheaper using microarrays instead of RNA-sequencing, the difference being in the range of 50 to 100 EUR/USD per sample. However, the benefits of RNA-seq can easily outweigh the extra cost.

The cost of RNA-sequencing (RNA-seq) ranges from approximately $36.9 to $173 for a single sample in an mRNA-seq experiment.

RNA-Seq has also been shown to be highly accurate for quantifying expression levels, as determined using quantitative PCR (qPCR)¹⁸ and spike-in RNA controls of known concentration²⁰.

These results clearly show that counting and normalization methods are the most critical steps in the RNA-seq analysis process. Particularly, considering the above results, we concluded that the combination of Trimmomatic + RUM + HTSeq Union + TMM was the most precise and accurate pipeline.

Advantages of RNA-Seq vs. qPCR. While qRT-PCR is useful for quantifying the expression of a few genes, it can only detect known sequences. In contrast, RNA sequencing (RNA-Seq) using NGS can detect both known and novel transcripts.

“mRNA-Seq offers improved specificity, so it's better at detecting transcripts, and specifically isoforms, than microarrays. It's also more sensitive in detecting differential expression and offers increased dynamic range.”

the inability to develop fast and accurate sequencing techniques.

Similar to ChIP-chip, ChIP-seq provides information about genome-wide protein binding. However, unlike ChIP-chip, ChIP-seq uses NGS technology to identify DNA fragments and map them against the entire genome.

Single-end reads are often used for typical ChIP-seq analyses, while paired-end ones improve the library complexity and increase mapping efficiency at repetitive regions [38]. When research focuses on repetitive regions, longer and/or paired-end reads are preferred.

ChIP-Seq experiments typically require one to ten million cells resulting in 10–100 ng of ChIP DNA.

What is the difference between single nuclear and single cell RNA seq? ›

Difference between snRNA-seq and scRNA-seq

That is to say, scRNA-seq measures both cytoplasmic and nuclear transcripts, while snRNA-seq mainly measures nuclear transcripts (though some transcripts might be attached to the rough endoplasmic reticulum and partially preserved in nuclear preps).

Since it has high rate of multiplication, large biomass can be produced. It helps reduce pollutants. It is not affected by weather conditions.

RNA sequencing strandedness allows researchers to determine which DNA strand (sense or antisense) a transcript came from. Compared to regular RNA sequencing methods, stranded RNA sequencing can find novel transcripts, distinguish transcripts from overlapping genes, find antisense sequences, and annotate genes.

There are two reasons why RNA is generally single-stranded: RNAses are extremely abundant in most cells. Usually, double-stranded RNA is associated with a viral infection and is destroyed rapidly. Double-stranded RNA is not as stable as double-stranded DNA.

Whereas DNA always occurs in cells as a double-stranded helix, RNA is single-stranded. RNA chains therefore fold up into a variety of shapes, just as a polypeptide chain folds up to form the final shape of a protein (Figure 6-6).

It is much more difficult and time-consuming to sequence proteins than DNA. Thanks to the genetic code, the protein sequence can be deduced from the DNA sequence (but not vice versa, because most amino acids are encoded by more than one codon, see earlier section).

The most popular methods are hybridization capture, amplicon sequencing, and molecular inversion probes (MIPs). For more in-depth comparison of hybridization capture and amplicon sequencing, see our Targeted Sequencing Guide. Whole exome sequencing identifies all the protein-coding genes in the genome.

Genetic sequencing company Illumina has unveiled a new machine it says can sequence the human genome for just $200, Wired reported Sept.

Is Illumina cheaper than Nanopore? ›

For example, the estimated cost per Gb for the Illumina NovaSeq 5500 is $50-$63, while for the Oxford Nanopore PromethION the cost estimate is between $21-$42⁵. It is also prudent to factor the sequencing instrument's maintenance requirements and costs into your decision.

In contrast to the bulk RNA-seq where the average gene expressions are measured across a large population of cells, scRNA-seq quantifies transcriptome of individual cells.

Number of cells per sample

Single-cell sequencing projects can be anywhere between a few hundred to 10,000 cells per sample.

Typically, we recommend a sequencing depth between 30,000 and 70,000 reads per cell for 10x Genomics projects.

A higher sequencing depth generates more informational reads, which increases the statistical power to detect differential expression also among genes with lower expression levels. For that reason, many published human RNA-Seq experiments have been sequenced with a sequencing depth between 20 M - 50 M reads per sample.

Recommendations for RNA-seq experiment design

At least six replicates per condition for all experiments. At least 12 replicates per condition for experiments where identifying the majority of all DE genes is important.

The standard protocol for library construction requires between 100 ng and 1 μg of total RNA. There are kits available for ultra-low RNA input that start with as little is 10 pg-10ng of RNA; however, the reproducibility increases considerably when starting with 1-2 ng.

Pathway analysis is a set of widely used tools for research in life sciences intended to give meaning to high-throughput biological data. The methodology of these tools settles in the gathering and usage of knowledge that comprise biomolecular functioning, coupled with statistical testing and other algorithms.

You need a Pathway Analysis – when you care about how genes are known to interact. The crucial difference between a gene set and a pathway is that a gene set is an unordered collection of genes whereas a pathway is a complex model that describes a given process, mechanism or phenomenon.

How does pathway enrichment analysis work? ›

Pathway enrichment analysis (PEA) is a computational biology method that identifies biological functions that are overrepresented in a group of genes more than would be expected by chance and ranks these functions by relevance.

The KEGG pathway representation focuses on the network of gene products, mostly proteins but including functional RNAs. As illustrated in Figure ​2, the metabolic pathway is a network of indirect protein–protein interactions, which is actually a network of enzyme–enzyme relations.

GO stands for Gene Ontology and as the name suggests, it annotates genes using an ontology. KEGG, Panther and other "pathway" databases group genes into "pathways" which are basically lists of genes participating in the same biological process.

“A sequence spells out the order of every DNA base in the genome, while a map simply identifies a series of landmarks in the genome,” it said. “Sometimes mapping and sequencing are completely separate processes. For example, it's possible to determine the location of a gene — to 'map' the gene — without sequencing it.

The crucial difference between a gene set and a pathway is that a gene set is an unordered collection of genes whereas a pathway is a complex model that describes a given process, mechanism or phenomenon.

The three types of maps — linkage, chromosomal, and physical — are illustrated in Figure 7.1 and are distinguished both by the methods used for their derivation and the metric used for measuring distances within them.

The sequence tells scientists the kind of genetic information that is carried in a particular DNA segment. For example, scientists can use sequence information to determine which stretches of DNA contain genes and which stretches carry regulatory instructions, turning genes on or off.

Pathway analysis is a set of widely used tools for research in life sciences intended to give meaning to high-throughput biological data. The methodology of these tools settles in the gathering and usage of knowledge that comprise biomolecular functioning, coupled with statistical testing and other algorithms.

PathVisio. "PathVisio is a free open-source pathway analysis and drawing software which allows drawing, editing, and analyzing biological pathways. It is developed in Java and can be extended with plugins." PathVisio is freely available for download.

Fundamentally, GSEA is an analysis method and the Gene Ontology is a dataset. There are two different types of entities present in GO: i) genes (or other macromolecules - transcripts, proteins etc); and ii) GO terms.

What is the difference between Ora and GSEA? ›

ORA methods differ from GSEA because they only consider the query gene set of interest and need a strict cutoff to classify genes as up- and down-regulated; thus, it is advisable to choose GSEA methods when there is uncertainty about the cutoff value.

Highly recommend!! I love how easy Kegg is to use and honestly feel like it's the most reliable tool I've had in my pregnancy journey. I actually became pregnant on my 2nd or 3rd cycle using Kegg, unfortunately that pregnancy resulted in miscarriage.

kegg is best suited for women who are trying to get pregnant, have regular ovulatory cycles of 21-40 days in length, are off hormonal birth control or IUD and their ovulatory cycles have returned, and are at least 6 weeks postpartum and have had at least two menstrual cycles.

It is an ontology database containing hierarchical classifications of various entities including genes, proteins, organisms, diseases, drugs, and chemical compounds.