The Beginner's Guide to RNA-Seq - #ResearchersAtWork Webinar Series

Summary

El webinar presenta una introducción a RNA-seq, una técnica avanzada para estudiar la expresión génica a través de la secuenciación de alta capacidad. Cubre desde conceptos básicos hasta el diseño de experimentos, análisis y interpretación de datos. Se destaca la importancia de la normalización de datos y se mencionan estudios significativos que han utilizado RNA-seq, como el Proyecto Cancer Genome Atlas. Además, se ofrece un descuento promocional y se invita a la audiencia a participar en la sección de preguntas y respuestas.

Takeaways

• 📘 El webinar comenzó con una introducción a RNA-seq para aquellos recién llegados, enfocándose en el uso constante y sistemático de los productos y servicios de la empresa.
• 🌐 Se compartieron detalles sobre cómo, tras el webinar, se proporcionarían diapositivas y grabaciones para los asistentes, y se señaló la importancia de registrarse para recibir esta información.
• 🔬 Se presentó a los oradores, Dr. Christopher y Battery, expertos en aplicaciones científicas y desarrollo de productos con experiencia en áreas como la biología celular y la terapia génica.
• 🏢 Se habló sobre Applied Biological Materials (ABM), fundada en 2004, con sede en Vancouver, Canadá, y su misión de catalizar descubrimientos científicos en la vida y el desarrollo de medicamentos.
• 📈 Se mencionó el crecimiento de ABM y sus instalaciones, destacando su compromiso de ofrecer un servicio de clase mundial a sus clientes.
• 🧬 Se exploraron los antecedentes de RNA-seq, desde el northern blot en 1977 hasta el desarrollo de microarrays y la aparición de RNA-seq, que permite la detección de ARN y el estudio de la expresión génica.
• 🔍 Se discutieron las ventajas de RNA-seq, como la alta resolución y la capacidad de secuenciar transcriptomas nuevos sin datos previos, en comparación con métodos anteriores.
• 🧬 Se abordó la importancia de la selección de ARN en la preparación de la muestra para RNA-seq, ya sea a través de enriquecimiento de ARN poli-A o la eliminación de ARN ribosomal.
• 🔬 Se explicó el flujo de trabajo general de RNA-seq, incluyendo la preparación de la librería, la PCR de enlace, la secuenciación por síntesis y el análisis de datos.
• 📊 Se mencionaron las técnicas de análisis y normalización de datos RNA-seq, como el uso de fragments por kilobase de transcripto por millones de lecturas mapeadas (FPKM) para comparar niveles de expresión genética.
• 🌟 Se destacaron estudios como ENCODE y el Cancer Genome Atlas Project, que han utilizado RNA-seq para entender mejor los mecanismos subyacentes de la enfermedad y el cáncer.
• 💡 Se ofreció un código promocional del 25% de descuento en el paquete de bioinformática de secuenciación de ARN y se invitó a los asistentes a contactar a ABM para obtener más recursos y soporte técnico.

Q & A

¿Qué es RNA-seq y cómo se relaciona con la regulación de la expresión génica?

RNA-seq es una técnica que permite identificar y cuantificar la expresión deARN en una muestra biológica en un momento dado, lo que permite investigar los cambios en la expresión génica y detectar eventos de esplicación alternativa y nuevos genes.

¿Qué es el propósito de los webinars de Applied Biological Materials (ABM)?

Los webinars de ABM están diseñados para proporcionar capacitación constante y sistemática que ayuda a mantener a los participantes informados sobre los productos y servicios de la empresa.

¿Qué áreas de investigación tiene experiencia Christopher, el especialista en aplicaciones científicas de ABM?

Christopher tiene casi 10 años de experiencia en investigación y diseño experimental con investigadores de todo el mundo en áreas que van desde la biología celular y el desarrollo hasta la validación de CRISPR y la ingeniería de líneas celulares.

¿Qué es el propósito de la sesión de preguntas y respuestas al final del webinar?

La sesión de preguntas y respuestas permite a los participantes hacer preguntas relacionadas con el contenido del webinar y recibir respuestas directas de los expertos para aclarar dudas y obtener información adicional.

¿Cuál es la importancia de la normalización de datos en el análisis de RNA-seq?

La normalización de datos es crucial para ajustar las diferencias en la cantidad de lecturas por muestra y por gen, lo que permite comparar la expresión génica de manera justa y precisa entre muestras y condiciones experimentales distintas.

¿Qué es FPKM y cómo ayuda a normalizar los datos de RNA-seq?

FPKM (Fragments Per Kilobase of transcript per Million mapped reads) es una métrica que ayuda a normalizar los datos de RNA-seq, considerando la cantidad de fragmentos de cDNA y la longitud del gen, para estimar la expresión génica de manera relativa.

¿Cómo se realiza la selección de ARN mensajero (mRNA) durante la preparación de la muestra para RNA-seq?

La selección de mRNA se puede realizar a través de la enriquecimiento con polia (poly A enrichment) o la eliminación de ARN ribosomal (rRNA depletion), dependiendo de los objetivos del estudio y el tipo de muestra.

¿Qué es la profundidad de secuenciación y por qué es importante en RNA-seq?

La profundidad de secuenciación se refiere a la cantidad total de lecturas de ARN que se obtienen en un experimento RNA-seq. Es importante porque determina la cantidad de información que se puede obtener sobre la expresión génica y la calidad de los resultados.

¿Qué es el Proyecto Genoma Atlas del Cáncer y cómo ha utilizado RNA-seq en sus investigaciones?

El Proyecto Genoma Atlas del Cáncer es un esfuerzo para analizar miles de muestras de pacientes con cáncer utilizando RNA-seq, con el objetivo de entender mejor los mecanismos subyacentes de la transformación y progresión del cáncer.

¿Cómo se puede utilizar RNA-seq en la medicina personalizada y qué impacto puede tener en las enfermedades genéticas?

RNA-seq puede ampliar el trabajo en medicina personalizada al proporcionar información detallada sobre la expresión génica y la regulación en respuesta a diferentes condiciones y tratamientos, lo que puede tener un impacto significativo en el diagnóstico y el tratamiento de enfermedades genéticas.

Outlines

00:00

😀 Introducción al webinar sobre RNA-seq

El primer párrafo presenta el inicio del webinar y agradece la paciencia de los asistentes. Se da la bienvenida y se menciona el tema principal, RNA-seq, para aquellos recién llegados. Los webinars tienen como objetivo proporcionar capacitación constante y sistemática sobre productos y servicios. Después del webinar, se compartirán diapositivas y la grabación. Se destaca la opción de hacer preguntas a través de una cuenta de Google y se menciona un enlace de registro para asegurar la recepción de material adicional. Se presenta un esquema de los temas a tratar, desde la expresión génica hasta la interpretación de experimentos RNA-seq. Finalmente, se presentan los oradores, Dr. Christopher y Battery, expertos en aplicaciones científicas y desarrollo de productos con amplia experiencia en investigación.

05:03

🌟 Desarrollo histórico y ventajas de la técnica RNA-seq

Este párrafo detalla la evolución de las técnicas para estudiar la expresión génica, desde el northern blot en 1977 hasta la aparición de RNA-seq. Se discuten las limitaciones de los métodos anteriores, como la necesidad de conocer previamente las secuencias de ADN o ARN, y se contrastan con las ventajas de RNA-seq, que incluyen una alta resolución y la capacidad de secuenciar transcriptomas sin datos previos. Además, se menciona el aumento exponencial en las publicaciones sobre RNA-seq desde su introducción, lo que demuestra su utilización y accesibilidad para los investigadores.

10:04

🔬 Consideraciones para el diseño de un experimento RNA-seq

El tercer párrafo se enfoca en aspectos importantes a considerar al diseñar un experimento RNA-seq. Se discute la composición del ARN total en las células y la necesidad de enriquecer la muestra con el material de interés, como el ARN mensajero, mediante técnicas como la enriquecimiento con polia o la depuración de ARN. También se abordan las decisiones a tomar en cuanto a la profundidad de secuenciación y el uso de secuenciación de un extremo o ambos extremos, dependiendo de los objetivos del proyecto.

15:05

🛠️ Flujo de trabajo general de RNA-seq y tecnologías de secuenciación

Aquí se describe el flujo de trabajo general de un proyecto RNA-seq, desde la preparación de la biblioteca hasta el análisis de datos. Se mencionan los pasos de fragmentación, ligación de adaptadores, generación de clusters y secuenciación por síntesis. Se destaca la importancia de la PCR puente y se explica cómo se evitan los problemas de sobre-o sub-agrupación. Además, se introducen las tecnologías de control de calidad, como el qubit, el Agilent bioanalyzer y el qPCR, y se describe cómo se utiliza la tecnología de secuenciación por síntesis para leer las secuencias ADN.

20:07

📊 Análisis y interpretación de datos RNA-seq

El quinto párrafo se centra en el análisis y la interpretación de los datos obtenidos a través de RNA-seq. Se explica el proceso de conversión de datos en formato fastq y se discuten las etapas de alineación de lecturas, normalización y generación de visualizaciones como mapas de calor y análisis de componentes principales. Se introduce el uso de métricas como las piezas por kilobase de transcripto por millones de lecturas mapeadas (FPKM) para la normalización de datos y se describen las diferentes técnicas de análisis, como la anotación funcional de genes diferencialmente expresados.

25:07

🏥 Aplicaciones de RNA-seq en la investigación y la medicina

Este párrafo presenta ejemplos de estudios que han utilizado RNA-seq, como el Proyecto Encode, el Proyecto Model Organismo Encode y el Atlas del Genome del Cáncer. Se destaca cómo RNA-seq ha permitido avances en la medicina personalizada y en la comprensión de las bases moleculares de enfermedades como el cáncer. Se ofrece un código promocional para un descuento en el paquete de bioinformática de secuenciación de ARN y se mencionan los recursos educativos y el soporte técnico que ofrece ABM.

30:08

📞 Preguntas y respuestas sobre RNA-seq y servicios de ABM

En el último párrafo, se abordan preguntas comunes relacionadas con el proceso de RNA-seq, como el tiempo que toma la preparación de la biblioteca y el proceso de secuenciación, la posibilidad de realizar análisis con datos brutos proporcionados por el investigador y el manejo de muestras que no pasan la calidad. Se menciona la capacidad de ABM de realizar análisis personalizados y se ofrece una visión general de los recursos y el soporte que la empresa proporciona a sus clientes.

Keywords

💡ARN

ARN, o Acido Ribonucleico, es una molécula presente en todas las células vivas que desempeña un papel crucial en la transmisión de la información genética y en la síntesis de proteínas. En el video, se discute cómo el ARN se utiliza en el análisis de expresión génica, donde se puede estudiar su presencia y cantidad en una muestra biológica para entender los cambios en la expresión génica a lo largo del tiempo.

💡RNA-seq

RNA-seq es una técnica de secuenciación de próxima generación que permite medir la cantidad y la variedad de ARN en una muestra de tejido o células. Es fundamental para entender la expresión génica y se menciona en el video como el tema principal del webinar, donde se ofrece una introducción a esta tecnología y se discuten su aplicación y análisis.

💡Secuenciación de próxima generación (NGS)

La Secuenciación de Próxima Generación, o NGS, es una tecnología que permite secuenciar millones de fragmentos de ADN o ARN simultáneamente. En el video, se presenta NGS como el método subyacente detrás de la técnica RNA-seq, permitiendo el estudio detallado de la expresión génica y la identificación de eventos de esplicación alternativa.

💡Diseño del experimento

El diseño del experimento es un aspecto clave en la realización de un estudio RNA-seq, como se destaca en el video. Esto incluye la consideración de factores como el tipo de muestra, la profundidad de secuenciación y el uso de técnicas como la enrichment de ARN o la eliminación deARN para ribosoma, que son esenciales para obtener datos confiables y significativos.

💡Análisis de datos

El análisis de datos es un componente esencial en el proceso de RNA-seq, que se aborda en el video. Incluye la normalización de datos para corregir diferencias en la cantidad de lecturas por muestra y por gen, lo que permite comparar la expresión génica entre muestras de manera justa. El análisis también puede involucrar técnicas como mapas de calor y análisis de componentes principales.

💡Normalización de datos

La normalización de datos es un paso crítico en el análisis de RNA-seq, mencionado en el video, que corrige las diferencias en la cantidad de lecturas por muestra y por gen. El término FPKM, que se utiliza para describir la expresión de un gen en términos de fragmentos por kilobase de transcripción por millón de lecturas mapeadas, es un ejemplo de cómo se realiza esta normalización.

💡Annotación funcional

La anotación funcional se refiere al proceso de identificar los roles biológicos de los genes y cómo se relacionan con procesos o caminos moleculares específicos. En el video, se menciona como una parte del análisis de RNA-seq, permitiendo a los investigadores entender el significado biológico de los cambios en la expresión génica.

💡Estudio ENCODE

El Proyecto ENCODE es un esfuerzo en curso para identificar las regiones reguladoras en el genoma humano y se menciona en el video como un ejemplo de aplicación del RNA-seq. Este proyecto ha utilizado la tecnología de RNA-seq para generar un mapa detallado de los elementos funcionales en el genoma.

💡Transcriptoma

El transcriptoma hace referencia al conjunto completo de ARN producido por un organismo, incluyendo mRNA, ARNr y otros tipos de ARN. En el video, el término se utiliza para describir el objetivo de la secuenciación de RNA-seq, que es obtener una visión completa de la expresión génica en un momento dado.

💡Bioinformática

La bioinformática es el campo interdisciplinario que se enfoca en el análisis y la interpretación de datos biológicos, como los generados por la RNA-seq. En el video, se menciona la promoción de un paquete de bioinformática para el análisis de datos de RNA-seq, destacando la importancia de esta disciplina para la interpretación de los resultados.

Highlights

Webinar begins with an introduction to RNA-seq for new attendees, outlining the format and benefits of the training.

Post-webinar resources include PowerPoint slides and recorded webinar for continued learning.

Google account sign-in enables live Q&A during the webinar through the chat box.

Registration ensures receipt of slides and post-webinar question responses.

Webinar topics include gene expression background, NGS intro, RNA-seq experiment design, workflow analysis, and case studies.

Speaker introductions feature Dr. Christopher and Battery, specialists with extensive research experience.

ABM's mission is to catalyze scientific discoveries in life sciences and drug development.

Historical context of gene examination methods leads to the current RNA-seq technology.

Advantages of RNA-seq include single nucleotide resolution and ability to sequence new transcriptomes without prior data.

Illumina's 2007 advent correlates with an exponential increase in RNA-seq publications, indicating widespread use.

Next-generation sequencing (NGS) basics are explained, including read types and considerations for project goals.

RNA-seq experiment design considerations include enriching for mRNA and choosing sequencing depth and read length.

General RNA-seq workflow involves library preparation, bridge PCR, sequencing, and analysis.

Sequencing by synthesis technology and quality control measures are discussed for ensuring accurate results.

Data analysis post-sequencing includes converting raw data to fastq, alignment, normalization, and various analytical techniques.

FPKM is introduced as a metric for normalizing gene expression data across samples and gene lengths.

Examples of RNA-seq applications include large-scale projects like ENCODE and its impact on personalized medicine.

ABM offers promo codes, resources, and support for RNA sequencing and bioinformatics packages.

Webinar concludes with a Q&A session addressing practical questions about RNA-seq processes and services.

Transcripts

hello everyone thank you all for your

patience and we'll be starting the

webinar up now so good morning and thank

you for taking the time to join our

webinar today today's topic will provide

you with an introduction to RNA seek for

those of you who are new to our webinars

they're designed to provide constant and

systematic training that will help keep

you in whelmed formed on our products

and services following the webinar we'll

be sharing a copy of the PowerPoint

slides as well as the recorded webinar

itself I would like to point out that if

you're signed into a Google account you

can ask any questions you may have in

the chat box to the right of the screen

please know that if you did not register

earlier there's a registration link in

the video description

this will ensure that you get a copy of

the slides and answers to any questions

post it in the chat during the webinar

to help frame this webinar outline here

are the topics that we'll be covering

first will be we'll begin with some

background on gene expression followed

by an intro into NGS after that we'll

discuss some of the important

considerations when designing your RNA

seek experiment followed by

understanding the workflow analysis and

interpretation of your experiment

finally we'll take some time to review

some projects that have shown the power

and versatility of RNA seek before we

begin talking about RNA seek I'd like to

take a moment to introduce your speakers

joining me today is my colleague dr.

Chris Christopher's mention a scientific

application specialist here at ABM after

completing his PhD at UBC where he

studied stem cell regulation he joined

ABM with a passion for helping

scientists achieve their goals with

nearly 10 years of experience in

research and experimental design with

researchers around the world and in

areas ranging from cell and

developmental biology to CRISPR

validation and cell line engineering he

can assist with nearly every product

with M nearly any

project you have from initial set up to

post sequencing data analysis alongside

him you have myself battery battery a

product development specialist with over

five years research experience in areas

such as drug delivery lipid biosynthesis

and gene therapy obtained over the

course of my graduate degree at SFU our

main goal is to work closely with

clients and provide them with the

support that ABM has been renowned for

before we begin with the core content of

the webinar I'd like to take a moment to

talk about applied biological materials

and what our goals are

ABM was founded in 2004 and has been

driven to catalyze scientific

discoveries in the field of life science

and drug development for the last 15

years headquartered in Vancouver Canada

we are one of the fastest growing

biotech companies in the region and

since our inception we have worked hard

to become known as a reliable source for

clients such as yourselves this hard

work has allowed us to expand our

facilities beginning with a branch in

Chang su Province in China in 2013 and a

new facility in Bellingham that will be

opening up later in 2019 these

expansions have put us in a position to

work with each and every one of you and

provide the world-class service you all

deserve with our team of passionate and

trained scientists iBM is dedicated to

empowering researchers with the latest

innovations for all their scientific

needs now before we discuss the core

details of this webinar I feel it's

important to discuss some of the work

that led us to RNA seek as well as it's

important to enter research in general

when studying the role of genes in

development of disease there are three

distinct levels we can use for

examination we can study them at the DNA

level by studying mutations to determine

their effects on gene at the RNA level

by studying gene expression and at the

protein level by examining folding

patterns of genes the first link in the

chain leading to RNA seek is the

northern bloc

developed in 1977 by James Alwyn David

Kemp and George stark at Stanford

University

this tool was extremely useful and that

it allowed for the study of gene

expression via RNA detection following

the northern blot was rtq PCR which was

developed in the early 80s by Kary

Mullis and this technique allows for the

detection characterization and

quantification of RNA transcripts

finally the last step prior to RNA seek

was the microarray which was developed

by Patrick Brown at Stanford University

this tool is impressive and then it can

be used to simultaneously study the

expression levels of thousands of genes

at one time and with that we finally

come to the present RNA seek which

allows us to reveal the presence and

quantity of RNA in a biological sample

at any given moment as well as to look

at the changes in gene expression over

time now when considering these three

prior methods we can see that while they

all had strengths such as the low

reagent cost and the ability to be

easily done in the comfort of your own

lab there are some items that we should

consider a key note is the fact that

each system required prior knowledge of

the chain or the mRNA in order to be

used making it impossible to use these

methods to study novel genes with RNA

seek on the other hand the pros are

considerable well the initial cost of

RNA seek is high relative to the

previous methods the use of single

nucleotide resolution alongside the

ability to sequence new transcriptomes

without prior data is a huge bonus when

coupled with its massively high

throughput this creates a winning

combination that would be an asset to

any researcher now with the advent of

Illumina in 2007 we can see the near

exponential increase in the number of

RNA seek publications which demonstrates

the widespread use and accessibility of

this tool to researcher

and now we find ourselves in a position

to answer the question what is RNA seek

in essence RNA seek is a technique that

allows us to begin with cells or tissues

and examine the expressed genes by

taking advantage of next-generation

sequencing we can learn about changes in

gene expression and identify novel

splicing events and genes with this

brief background I'd like to pass things

over to Christopher who will begin with

a brief overview of - the topics for

today's talk but first a quick reminder

to everyone listening to please post any

questions you may have in the in the

chat box do during the course of the

webinar

thank you boshy for that brief

introduction and thanks for joining us

today for our webinar

I'm gonna go briefly into an

introduction to next-generation

sequencing to give you a better idea of

how the technology works before going

into more details about RNA seek so

next-generation sequencing can be used

for a variety of purposes including

whole genome sequencing studying changes

in gene expression using RNA seek or

performing metagenomic studies with

environmental samples the basic workflow

for all next-generation sequencing is

the same where you take input material

whether it's DNA or RNA fragment it to

be of a similar size ligate sequencing

adapters that can then bind to the

sequencer before undergoing

high-throughput sequencing now there are

a couple important terms to know for all

next-generation sequencing approaches

the first is the read a read is a

sequence of nucleotides that will be

sequenced so on the right you can see

that there's a double-stranded DNA

molecule and when it's sequenced it will

sequence along one strand which gives

you the sequencing read for the

nucleotides that are present we'd refer

this as single and sequencing since

you're only synthesizing one strand in

this case the sense strand alternatively

you can also use paired-end sequencing

to read a fragment

both strands of the molecule when you're

trying to decide which option to pick

for your project it's important to

consider what your actual project goal

is for instance single end sequencing is

generally sufficient to study changes in

gene expression whereas paired end

sequencing is more useful for whole

genome sequencing studying alternative

splicing or de novo transcriptome

studies next the read can vary in length

the read length is considered to be the

number of nucleotides that are sequenced

per read to give you an idea of the read

length sizes typically for RNA seek 75

nucleotides is a common read length

which would be great for studying gene

expression or resequencing samples 150

nucleotides is a longer read length

that's more suitable for assembling new

transcriptomes

or whole genome sequencing for

eukaryotes and even longer reads such as

300 nucleotides are more suitable for

amplicon seek as well as metagenomic

studies next once you have a sequencing

read you still have to figure out how it

aligns to a reference sequence so in

this example you can see that there's a

reference sequence in dark blue along

the bottom of the image and then the

read and light blue which maps to a

specific location in that reference

sequence if we were to look at a

specific nucleotide such as this G here

highlighted with a red box you can see

that there are two reads in light blue

that map to this eight and the reference

sequence because there are two reads

covering this we describe this as 2 X

coverage because you cover that

nucleotide with two different readings

if we look at another regions such as

this C residue you can see that there

are four reads that map to this specific

location this would give it a coverage

of 4x oftentimes there are also sites

which don't have any reads that mapped

them at all such as this a residue and

for this site you could describe it as

having zero x coverage now when you take

all

of these sites and add the levels of

coverage for each nucleotide together

you can make an idea of the coverage for

that site so in this example there are

six reads for these three bases G a and

C when you divide it by the number of

nucleotides you would get 2x coverage so

you could say that the sequencing depth

is 6 reads with an average of 2x

coverage for these nucleotides now for

most samples you would typically

sequence millions of reads to make sure

as much of the entire transcript time as

possible is sequenced typically bigger

genomes or transcript rooms require more

reads so for instance bacteria which

have a genome size of about 5 million

base pairs would require fewer reads and

less sequencing than mammals that would

have a 3 billion base pair genome or

plants which would even have much larger

genome sizes in terms of RNA seek for

bacteria this would look like 8 million

reads per sample versus 20 million reads

or 40 million reads per sample from

mammals and plants next I'm gonna go

over a couple important things to

consider when designing your rna-seq

experiment first most of the RNA that's

present in a cell is not actually

messenger RNA that is what most

researchers want to sequence for

instance if you take the total RNA

present and look at the breakdown of

what it's composed of you'll find that

about 85 percent of it is ribosomal RNA

which most researchers typically do not

want a sequence next about 10 to 12

percent is transfer RNA again which most

researchers are not interested in

sequencing for their projects mRNA

itself is typically about 2 to 3 percent

of the total RNA present in a cell and

then an even smaller percentage than

this is composed in micro rna's long

non-coding rnas circular RNAs and other

sequences so if your starting point is

this large population of total RNA you

have to find a way to enrich for what

you actually want a sequence in that

sample there are two basic ways that we

can do this the first of which is poly a

enrichment where we use special beads

that can bind to the poly a tails on

mrnh

and scripts to pull them out of the

population of total RNA and enriched for

poly a sequences alternatively if you

want to study mRNAs as well as small

RNAs like micro RNAs

you can use a treatment called our RNA

depletion which will selectively remove

the are RNA sequences from the total RNA

population if you're working with

eukaryotic samples you can use either

option poly a enrichment or RNA

depletion if you're working with

prokaryotic samples however you

absolutely have to use an RNA depletion

treatment because prokaryotic cells

typically do not have poly a tails on

their mRNA transcripts next you need to

consider how you're designing your

project and what your goal is if your

goal is to Nauvoo transcriptome assembly

for a species which previously hasn't

had a sequence transcriptome you

typically want greater sequencing depth

and longer reads to help with the

assembly next if you only want to study

changes in gene expression to see if a

particular gene increases or decreases

in response to a stimulus using single

end sequencing is typically sufficient

finally if you want to identify novel

transcripts or new alternative splicing

events you would typically want to use a

greater sequencing depth and paired end

sequencing next I'm gonna go over

briefly the general RNA seek workflow

for most projects this is highlighted

here and I don't want you to focus too

much on this but there are four basic

steps beginning with library preparation

before a sample is input into the

sequencer followed by bridge PCR

sequencing by synthesis and finally

analysis at the outset of the project

you'd have to input your starting

material assess its quality and convert

it to DNA typically when we begin with a

sample we would ask is the mRNA degraded

if it's not degraded we can perform poly

a selection before converting the RNA

into DNA this conversion from RNA into

DNA is done to increase this

ability of the molecule and ensure

success with sequencing if the mRNA

transcript is degraded however we can do

special treatments that can repair the

transcript to still provide sequence

below material for the project next once

the material has been converted into DNA

it will likely be of different sizes

because different mRNA transcripts are

generally of different lengths so the

next step would be to fragment the DNA

into uniform sizes to make sure each

fragment is equally likely to be

sequenced the next step is to ligate

sequencing adapters so that the DNA

fragments can bind the sequencer itself

once this is done you can input the

material into the sequencer and you'll

go through a step called cluster

generation or bridge PCR now oh sorry

I'm just gonna jump back briefly bridge

PCR is one of the most important steps

for sequencing because when you're

sequencing these molecules it's not

possible to sequence a single DNA

molecule in the sequencer itself you

first have to generate clusters of

identical molecules that are then

sequenced together so in this diagram in

part one you can see a DNA molecule that

binds to the flow cell of the sequencer

the molecule then bends over and binds

to the other adapter on the flow cell

before DNA synthesis begins to form a

double-stranded DNA molecule you can see

this now in part 3 where the sequencing

reaction that generated the double

strand DNA occurs in step four you can

see that you now have two molecules in

two clusters this process is repeated

many times in panel five until you have

sufficient DNA molecules to be able to

sequence each cluster now importantly

you need to know the concentration of

your library so that you can avoid over

clustering over clustering happens when

the cluster density is too high this

leads to the Machine not being able to

accurately read each cluster to

determine what the DNA sequences

and generally leads to the entire

sequencing run failing the opposite

problem you also want to avoid which is

under clustering this happens when the

cluster density is too low and it leads

to an overall lower sequencing output

and again makes it hard for the

sequencer to read what the sequences at

that cluster next I'll briefly go over

aluminous technology for sequencing by

synthesis to give you an idea of how the

sequencing process itself actually works

so when you have the template DNA it

will have a primer that will prime it

for DNA synthesis that binds to the flow

cell next you have individual

nucleotides that have fluorescent dyes

bound to them which can be used during

the synthesis reaction in the next panel

you can see that the a nucleotide has

been added to the sequence

all the nucleotides that didn't bind

because they don't have a complementary

base on the other strand would be washed

away after the step occurs there be a

fluorescent emission and response to

light stimulus from the nucleotide

that's been added that will be imaged by

the sequencer after this step the

fluorescent editor the fluorescent site

is cleaved from this molecule washed

away and then the process repeats again

once this repeats a to the end of

sequencing you will then be able to have

the Machine go through and read each of

the fluorescent signals that occurred to

piece together what the sequencing was

for that molecule at every stage of

sequencing though quality controls

essential and there are three core

technologies we use to perform this QC

the first of which is qubit that can

measure DNA concentration the Agilent

bio analyzer which can assess your DNA

library and how well it's been

fragmented and then qpcr which can be

used to determine how much of the actual

prepared library is sequence of all so

you're starting one of the most

important things you can do is to run a

RN angel to determine if your material

is possibly degraded if you have a high

quality sample you should see a few

distinct bands in your gel versus if the

sample is degraded you'll typically see

a large smear in that Lane if you do

have this degradation we can use special

kits to recover degraded RNA or to

reverse formaldehyde modification to RNA

if your sample was fixed prior to

extraction and this can help provide you

with sequence little material for RNA

seek next if you have a low amount of

starting material the first thing you

have to do is actually know what this

quantity is and for that we can use

cubit to measure the concentration of

nucleic acids that are present in a

sample this is crucial for library

preparation if you do have a low amount

of starting material we can use special

kits that can amplify the mRNA that is

present using the poly a tail prior to

RNA seek next after you have the

material that's been converted from RNA

to DNA and you go through the

fragmentation step the Agilent

bioanalyzer comes in handy to tell you

how efficient the fragmentation was and

to give you an idea of the average

fragment size in your sample and then

after you've liked it at the adapters

successfully qPCR becomes essential and

I'll go through these two steps next

with Agilent when you input your sample

the data that you get from it is

effectively a graph that tells you how

big the fragment sizes are that are

present in your sample and their general

distribution so in this graph here you

can see that there are two peaks on the

left and right hand side of the graph

I've highlighted them here in red boxes

for you what you actually want to look

at though is the fragment size highlight

in this green box which tells you

roughly that your fragments are on the

higher end of the range of fragment

sizes which is a good result which you

want to have for sequencing the

alternative is when you have an uneven

distribution of fragment sizes with no

large fragments and typically these

smaller fragments are more difficult to

see

and are less likely to give you a

high-quality sequencing result or lead

to a failure in sequencing so we

generally want to avoid this next with

qPCR this gives you a helpful metric

that you can't measure with qubit

now remember qubit can tell you about

the amount of nucleic acids present in a

sample but qPCR can tell you how much of

the nucleic acids are actually

sequencing bull and have adapters like

age them correctly next I'm going to go

over a bit more of rna-seq analysis and

interpretation of the data

so the general workflow once the

next-generation sequencing has been

complete is that you will go from raw

data to a format called fast queue with

intermittent steps before you can do

data analysis the raw data from the

sequencer you will generally never see

because either software or the sequencer

itself will process it into fast queue

now fast queue is simply the FASTA

format a text-based format for depicting

nucleotides with the quality control

info from that sequencing run in that

particular sequencing read if you want

to see what this actual fast queue data

would look like shown here to the right

and you can see that it's largely a line

of alphanumeric text but it's kind of

hard to understand what's going on the

important sequence information here is

highlighted in the red box which is the

actual sequencing result from this

particular sample from this particular

cluster next you have to figure out how

to use the actual data before you can do

the analysis that you're interested in

so with fast queue this is generally

what we provide for each next-generation

sequencing project whether it's RNA seek

whole genome seek or another type of

sequencing if you wanted to work with

this you'd have to take fast queue data

first and align it to the reference

sequence we briefly went over the

alignment of reads

earlier in the presentation this is one

of the crucial steps at the end of

sequencing after you've done the

alignment which can generally be done

using different software you would then

have to normalize your data before you

can begin the

alysus and generate beautiful figures

that you can eventually publish with

your manuscript I'm gonna over a bit of

how the data is normalized it's

important to normalize your data for two

main reasons

but first is you need to normalize the

number of reads per sample because some

samples may receive different reads next

you need to normalize the number of

reads per gene because genes are

different lengths and you have to take

this into account I'll go over this a

bit more in the next few slides for

instance if you have three samples

sample a B and C you would generally

want each sample to be sequenced an

equal amount but because of stochastic

differences in sequencing that cannot be

controlled you generally have different

sequencing outputs for each sample in

this example sample a would have about

20 million reads sample B about 30

million reads and sample C would have 10

million reads if you didn't normalize

your data without doing any processing

you would think that samples C has

one-third of the gene expression of

sample B for instance or that sample a

and sample B's expression levels are

different even if they may be identical

secondly you need to normalize relative

to the length of the gene and the number

of reads for that gene in this example

you can see gene a is about one KB and

gene B is 2 KB or twice that length for

this example gene a has 5 reads that map

to it and gene B has 10 reads but gene B

is twice as long if you didn't normalize

your data you would think that gene B is

expressed twice as highly even if that

may not be the case so you'd then have

to normalize the data there's a useful

metric we can use that can account for

differences between samples and between

genes this is called fragments per

kilobase of transcript per million

mapped reads or fpkm this is a way to

look at the relative expression of a

transcript proportional to the number of

cDNA fragments that it originated from

or sorry that originated from it so fpkm

is essentially the normalized estimation

gene expression based on the RNA

sequencing data or another way to think

of this is fpkm helps normalize the

differences in the amount of reads per

sample as well as the number of reads

proportional to how long a given gene is

in this specific example once we've

normalized gene a and gene B you can see

that the fpkm value is 2 for both

indicating that both these genes despite

having different lengths and different

numbers of mapped reads have the

relatively similar expression level if

you were to compare this to a treated

normalized sample you'd then be able to

include whether or not expression has

increased or decreased relative to

control using this normalized data set

next once you have this what type of

analysis can you actually do the first

is you can use heat maps to look at

large changes in expression between a

control sample and a tree example and to

visually detect depict increases or

decreases in gene expression next you

can perform principal component analysis

to see if any of your samples have

different changes in gene expression

that may correlate and be related to the

treatment conditions they underwent next

you can also do functional annotation of

differentially expressed genes with this

this allows you to look at genes that

have increased an expression decreased

in expression or remain the same and see

if there's any similar pathways or

processes that they might be involved in

such as metabolism inflammation or even

a mean response next I'm gonna hand it

back to boshy to go over a couple

examples of researchers that have used

RNA seek in their studies

thank you for that excellent talk

Christopher as he just mentioned I'll be

taking a few minutes to discuss some of

the studies that people have used to

demonstrate the power and versatility of

our NAC and after that we'll wrap up the

webinar and proceed to the Q&A session

now the first of these projects is the

encode project which focused on

identifying genome-wide regulatory

regions in different cell lines this was

then followed by the model organism

encode project which aimed to provide by

the biological research community with a

comprehensive encyclopedia of genomic

functional elements in the model

organism C elegans and D Milano gaster

this was a huge undertaking and was

aimed at helping to better understand

the downstream effects of regulatory

regions next we have the Cancer Genome

Atlas Project which took advantage of

RNA seek to analyze thousands of cancer

patient samples to better understand the

underlying mechanisms of malignant

transformation and progression and

cancer finally the use of RNA seek in

medicine has been substantial as it's

allowed for researchers to expand their

work into personalized medicine which

can have a huge impact on genetic

disease and with that we now come to the

wrap-up portion of the webinar and as a

thank you

we'd like to offer your promo code for

25% off of your RNA sequencing

bioinformatics package which you can see

here and will also be included in our

follow-up email and now that we've

finished through with the background of

RNA seek let's take a moment to examine

some of the resources that ABM offers

first make sure to check out our website

where all of our resources are collected

this includes our knowledgebase articles

as well as our YouTube videos and blog

posts to help provide you with the tools

you need for your experiments now not

only do we have a diverse selection of

educational material we also have an

incredibly knowledgeable technical

support

team who can guide you through your

experiments as well as a dedicated

customer service team to ensure that you

receive your items in a timely manner if

you have any questions about our

materials or services you can always

reach out to us by phone email online

chat and in addition we have a

comprehensive frequently asked questions

section on our website so you can always

browse through and see if you can find

the answers there thank you for your

taking the time to join us today you

just give you a heads up we'll be having

a for another webinar on whole genome

sequencing so keep an eye out for an

invitation from us soon thank you for

taking the time to join us today and now

we'll proceed over to the Q&A section

all right so you just take a moment give

us a moment please just to go through

all the questions and we'll start up

with as many as we can get through all

right so we've got one here from Ian oh

and the question is how long does it

take to do library prep as well as QC

and sequencing so Chris so generally

from the time we receive your samples to

do a preliminary QC the library prep

process the Agilent QC and prepare your

samples for sequencing the process from

the date we get your samples until you

get your data is about four to six weeks

now a large part of that time isn't set

aside for the actual sequencing process

which can generally be done in about a

week but it's to ensure that we have

sufficient samples in order to set up

the sequencing run if you have a large

number of samples we can possibly

provide you with the results from your

sequencing in a matter of a few weeks

that sound look good answer so now let's

look for another one we've got one here

from Andrew Cushman he's asking if if

he's done his experiment and he has the

raw data can he give us the raw data and

have us do the sequencing for him

thanks Andrew that's a great question so

if you do have raw data and you don't

know what to do with it we have a

dedicated in-house bioinformatics team

that can effectively do nearly any type

of analysis that you need for your

project we have a number of

bioinformatics services listed on each

of our NGS pages including for RNA seek

if there's something that you're

interested in that we don't list if you

simply send us an email we can work with

our bioinformatics team to set up a

custom analysis package for you and

generally get the results to you in a

matter of a few weeks depending on how

challenging the analysis is and how much

custom software we have to develop for

you that's perfect

all right now we've got another one here

from Kate what happens if my samples

fail QC Kris again thanks Kate for that

great question so if we receive your

samples and there's any issue during the

QC process we'll reach out to you and

let you know if we would like to ask for

new samples or if there's steps we can

take to try to address it and process

your samples and possibly still achieve

a high-quality sequencing results if for

whatever reason your samples don't pass

the additional QC we reach out to you

again and ask you if you'll be able to

provide new samples or provide with

alternative options for proceeding

that was a great answer Chris we're just

scrolling through the questions all

right so we have one more I think this I

think we've only got time left for one

more question

so we've got one here from and why she

Ann's asking if she can submit one

sample and use it for both RNA seek and

MI RNA seek or do they need to have do

they need to submit separate samples for

each one it's actually quite a popular

question so when we're trying to process

samples from micro RNA seek we need to

use special kits that are unique for

small RNA sequences this would be

different than what we would normally do

for regular RNA seek so if you did want

to do both rna-seq and micro RNA seek

we'd either ask for twice the starting

sample amount or two separate samples in

order to process this that was fantastic

and I hope that was helpful and that

made sense let me see if we've got we

have if we're able to answer any more

all right

so unfortunately I think we are a little

up on time right now so we'll do is

we'll go through the questions we'll

write up some answers and we'll be

emailing that out to you along with the

slides for this webinar so once again

thank you all for joining us today and

we hope you have a great rest of the day

thank you