小分子RNA介导的基因表达调控（导读版） mobi 下载 网盘 caj lrf pdf txt 阿里云

内源小分子RNA广泛存在于各种生物中，包括人类、小鼠、果蝇、蠕虫、真菌和细菌等。microRNA作为一种细胞调控关键因子能够修饰基因的表达。在高等真核生物中，microRNA甚至能调控约50%基因的表达。

本书汇集了众多科技工作者的前沿性工作，内容包括从细菌到人类等生物组织中microRNA调控途径的多样性。除了阐述调控小分子RNA的生物合成机制及其加工过程，作者还探讨了这些途径的功能在寄主体内的重要性。

本书围绕小分子RNA这一新发现的调控分子，针对其参与调控的广度与创新性进行了阐述。小分子RNA已经成为研究基因功能的强有力工具，并带来了一系列的重大发现，必将对增进基因功能与疾病治疗的理解带来革命性的改变。

前言

致谢

编者简介

撰稿人

第1章 MicroMining:通过计算方式发现未知的microRNA Adam Grundhoff

第2章 动物microRNA基因预测 Ola Snφve,Pal S*trom

第3章 研究microRNA存在与功能的一系列资源 Praveen Sethupathy,Molly Megraw, Artemis

G. Hatzige***iou

第4章 大肠杆菌Hfq结合小RNA对mRNA稳定性及翻译的调控 Hiroji Aiba

第5章 动物细胞巾microRNA调控基因表达的机制 Yang Yu,Timothy W. Nilsen

第6章 秀丽隐杆线虫microRNA Mona J. Nolde,Frank J. Slack

第7章 秀丽隐杆线虫小RNA的分离及鉴定 Chisato Ushida, Yusuke Hokii

第8章 MicroRNA与果蝇发育 Utpal Bhadra,Sunit KumarSingh,Singh,S. N. C. V.

L. Pushpavalli,Praveensingh B. Hajeri,Manika Pal-Bhadra

第9章 斑马鱼RNA干扰与microRNA Alex S. Flynt,Elizabeth J. Thatcher,James G.

Patton

第10章 植物microRNA的产生和功能 Zoltan Havelda

第11章 拟南芥内源小RNA途径 Manu Agarwal,Julien Curaba,Xuemei Chen

第12章 如何评价microRNA表达——技术指导 Mirco Castoldi,Vladimir Benes,Martina U.

Muckenthaler

第13章 MicroRNA基因表达定量的方法 Lori A. Neely

第14章 MicroRNA介导的可变剪切调控 Rajesh K. Gaur

第15章 RNA聚合酶Ⅱ介导的内含子microRNA表达系统研究进展 Shi-Lung Lin,Shao-Yao Ying

第16章 基于microRNA的RNA聚合酶Ⅱ表达载体在动物细胞RNA干扰中的应用 Anne B. Vojtek,Kwan-Ho

Chung,Paresh D. Patel,David L. Turner

第17章 转基因RNA干扰技术——一种用于哺乳动物反向遗传学研究的快速低成本方法 Linghua Qiu,Zuoshang

Xu

第18章 AIDS交响曲——基于microRNA的治疗方法 Yoichi R. Fujii

第19章 MicroRNA与癌症——连点成线 Sumedha D. Jayasena

第20章 哺乳动物巾小RNA介导的转录水平基因沉默 Daniel H. Kim, John J. Rossi

第21章 由RNA介导的转录水平基因沉默控制的基因表达调控 Kevin V. Morris

索引

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1 MicroMining

Computational Approaches

to microRNA Discovery

Adam Grundhoff

Overview....................................................................................

............................1

1.1

Introduction.......................................................................................................2

1.2 When Is a Small RNA an

miRNA?...................................................................2

1.3 Advantages and Disadvantages of Experimental versus

Computational

miRNA

Identification........................................................................................3

1.4 Computational Prediction of

miRNAs..............................................................5

1.4.1 Getting Started: Upstream

Filtering......................................................7

1.4.2 Following Through: Structure Prediction and

Scoring....................... 12

1.4.3 Wrapping It Up: Experimental

Validation........................................... 14

1.5 Viral

miRNAs.................................................................................................

15

1.6

Conclusi***......................................................................................................

16

References.................................................................................................................

16

Overview

The recent past has seen the rapid identification of thousands

of microRNAs

(miRNAs) encoded by various metazoan ***anisms as well as some

viruses, and it

is very likely that many more still await discovery. Most of the

hitherto-known miRNAs

have been identified via the cloning and sequencing of small

RNAs. While very

powerful, this approach is not without its limitati***:

especially those miRNAs that

are of low abundance, o***hich are only expressed in certain

cell types or only during

brief periods of ***anismal development, or are easily missed in

cloning-based

screens. Thus, alternative means of miRNA discovery are

needed.

Given that the signal that marks the miRNA precursor for the

cellular processing

machinery appears to be a relatively *** one (i.e., a hairpin

structure), and

c***idering the rapidly increasing availability of large-scale

genomic sequencing

data for many ***anisms, computational methods appear ideally

suited for the comprehensive

identification of hitherto-unknown miRNAs. This chapter

discusses the

general principles of computational miRNA identification

methods, examines their

advantages and disadvantages as compared to the cloning method,

and takes a look

at the various miRNA prediction algorithms that have been

developed recently.

1.1 I ntroduction

miRNAs are small (～22 nt) RNA molecules that are able to

regulate the expression of

fully or partially complementary mRNA transcripts. As described

in greater detail

elsewhere in this book, they are initially transcribed as part

of hairpin structures

within much larger precursor transcripts (the so-called primary

RNAs or pri-miRNAs).

Following excision of the stem-loops by the RNase III?like

enzyme Drosha,

the isolated hairpins (called precursor miRNAs or pre-miRNAs)

are exported to

the cytoplasm and further processed by the Dicer complex to

produce the mature,

single-stranded miRNA molecule. Recent evidence suggests that

plants and animals

encode a multitude of miRNAs, many of which are evolutionarily

c***erved. As of

this writing, it is still true that the majority of known miRNAs

have been identified

experimentally, that is, by cloning of small RNAs. However, this

method has certain

limitati***, and alternative means for the prediction of novel

miRNAs are therefore

increasingly sought.

The observation that pre-miRNAs form characteristic stem-loops

has spurred the

development of a number of computational approaches designed to

identify novel

miRNA candidates based on the prediction and ***ysis of

secondary structures.

Given the already complete or near-complete sequencing of w***

genomes from

many species, such approaches hold great promise for identifying

the full complement

of miRNAs encoded by a given ***anism. However, because the

precise set of

structural features that differentiate a pre-miRNA stem-loop

from the large number

of hairpins in the genome is not known, additional filters have

to be employed to

reduce the number of false-positive predicti***, and

experimental confirmation of

the remaining candidates is required. In this chapter, I will

compare the benefits

and disadvantages of computational miRNA prediction methods in

comparison to

the cloning method, review principles of the existing miRNA

prediction algorithms,

discuss the general challenges and pitfalls of in silico miRNA

identification, and

provide an outlook of what might be expected from these

approaches in the future.

Finally, I will c***ider a special application of the miRNA

prediction problem: the

identification of miRNAs in viral genomes.

1.2 W hen is a small RNA an miRNA ?

In order to devise approaches designed to identify miRNAs, be

they experimental

or computational, it is important to clearly define what an

miRNA is. In a biological

sense, such a definition is quite straightforward: an miRNA is

simply a small,

single-stranded regulatory RNA molecule that is generated from

its precursor molecules

via successive processing by Drosha and Dicer. It is much more

difficult,

however, to define practicable criteria that are readily

testable on an experimental

or computational basis and that can unequivocally identify a

candidate sequence as

a genuine miRNA. Following the realization that miRNAs represent

abundant molecules

expressed in a wide variety of ***anisms, a c***ortium of

researchers agreed

on a set of criteria that have to be fulfilled before a

candidate can be called a bona

fide miRNA.1 According to these guidelines, it is necessary to

provide evidence that

(1) the candidate sequence is expressed as an appropriately

sized RNA molecule in

living cells and, furthermore, does not stem from random

degradation (Expression

criteria), and (2) that the maturation of the candidate involves

processing by Drosha

and Dicer (Biogenesis criteria). The expression criteria are

preferentially satisfied by

detection of a distinct band of approximately 22 nt on a

Northern blot. Alternatively,

the ability to detect the molecule in a library of cloned,

size-selected RNAs is c***idered

sufficient evidence, especially if the library contains high

copy numbers of

the particular candidate sequence.

To satisfy the biogenesis criteria, the guidelines by Ambros et

al.1 call for experimental

proof of Dicer processing by dem***trating that increased levels

of the precursor

accumulate in cells with decreased Dicer expression. In

contrast, experimental

proof of Drosha processing is generally not required; instead,

it is sufficient to show

that the putative precursor transcript has the capacity to adopt

a secondary structure

that is likely to be amenable to Drosha processing. Of course,

given the incomplete

knowledge of the rules governing recognition of target mRNAs by

Drosha, it is not

known what exactly makes a given RNA structure amenable to

Drosha processing,

and (as will be discussed later) this complicates the

computational prediction

of miRNA candidates c***iderably. Based on the characteristics

of known miRNA

precursor structures, however, it is generally agreed that the

minimal requirements

are (1) the adopted structure is a hairpin that does not contain

many or large internal

bulges, and (2) the mature miRNA is to be found within the stem

(not the loop) part

of the hairpin.

Evolutionary c***ervation serves as a third biogenesis

criterion: As miRNAs

are often c***erved in closely related (and sometimes even in

distant) species,

phylogenetic c***ervation of the miRNA sequence itself as well

as its fold-back

structure is c***idered strong evidence that the candidate

sequence represents a

genuine miRNA. An ideal miRNA candidate would meet all of the

preceding criteria;

however, it is generally c***idered sufficient to provide

convincing evidence

for at least one criterion out of the two categories. Indeed,

because Dicer knockout

cells are not readily available for most ***anisms, and

effective knockdown of

Dicer is technically challenging, positive experimental proof of

Dicer processing

is rarely shown.

1.3 A dvantages and Disadvantages of Experimental

versus Computational miRNA Identification

The “traditional” approach to identifying miRNAs c***ists of

cloning of small RNA

moieties. Although several protocols for the efficient cloning

of such molecules have

been devised, they all rely on the common principle of ligating

linkers to size-fractionated

RNAs, followed by cDNA synthesis and typically PCR

amplification. The

obtained products are then either cloned (often after

concatamerization to increase

the information obtained in a single-sequence read) and

sequenced, or subjected

directly to massive parallel sequencing approaches (“deep

sequencing”). According

to the guidelines described earlier, these candidates are then

further evaluated to

ensure that the putative pre-miRNA sequence adopts an

appropriate hairpin structure

around the candidate. If this is the case, the candidate can

generally be c***idered a

bona fide miRNA, since the recovery of the clone from a small

RNA library already

satisfies the expression criterion (nevertheless, Northern blots

are often performed to

allow for proper quantification of the miRNA).

The cloning approach has been extremely successful, and although

increasing

numbers of miRNAs are being identified via computational means,

the majority

of confirmed miRNAs currently listed in the miRNA database

(miRBase, http://

microrna.sanger.ac.uk) still have been identified via this

method. One of the great

advantages of the cloning protocol is that it provides the

precise sequence of the

mature miRNA molecule. Therefore, in contrast to

hybridization-based methods,

even closely related miRNAs that differ in only one nucleotide

position can be distinguished.

Also, the currently available computational prediction tools

generally only

allow identification of miRNA precursors but do not reliably

predict the location of

Drosha and Dicer cleavage sites. In contrast, cloning identifies

the precise 5′ and 3′

termini of the mature miRNA molecule.

As it appears that nucleotides 2 to 8 of the miRNA (the

so-called seed region)

are especially important for target recognition, knowledge of

the precise ends (and

particularly the 5′ terminus) is a distinct advantage if a

computational prediction

of target transcripts is to be performed. As might be expected,

the frequency with

which a given miRNA is cloned often is approximately equivalent

to its abundance

(although this frequency may also be affected by other factors;

see the following text)

and therefore provides a rough estimate of its expression

levels. Thus, abundantly

expressed miRNAs are usually readily identified. However, it can

be challenging

to achieve a saturated screen that also captures rare miRNAs.

Furthermore, even if

such miRNAs are contained within the library, one can never be

entirely certain that

enough clones have been sequenced to identify all of them.

In addition to these c***traints, the scope of a cloning screen

is also limited by

its source material; naturally, only miRNAs that are expressed

in the cells from

which the RNA material was derived can be identified. Many

miRNAs, however,

are expressed in a tissue-dependent manner, or are only

expressed at certain developmental

stages. This limitation can be partially overcome for relatively

*** ***anisms,

where the RNA can be prepared from w*** animals (e.g., mixed

larvae stages

and ***s from worms or insects).

In ***anisms with higher complexity such as vertebrates,

however, the situation is

more difficult: RNA from different embryonic or *** tissues

can be mixed, but the

sensitivity of the screen will dramatically decrease with the

complexity of the source

material, and it is very unlikely that nonabundant miRNAs could

be identified in

such screens. While these problems could be theoretically solved

by massive screening

efforts, that is, performing separate screens with material

prepared from every

individual tissue at each developmental stage, the cloning

approach also appears

limited in a more fundamental way. Several observati*** suggest

that some miRNAs

are more readily cloned than others owing to intrinsic

properties such as sequence

composition, the presence of certain nucleotides at their

termini, or posttranscriptional

modificati*** such as methylation or RNA editing.2?6

Computational approaches to miRNA discovery are not subject to

many of the

limitati*** that apply to the cloning method. Certainly, one of

the biggest advantages

of computational miRNA identification is the universal scope of

the ***ysis; as the

prediction does not require experimental material, it can

potentially discover all of

the miRNAs encoded by a given ***anism, even those that are

expressed only at

very low levels, in rare cells, or during brief periods of

development. However, this

advantage is partially annulled by the insufficient precision of

the presently available

algorithms: as the programs (to varying degrees) produce large

numbers of falsepositive

predicti***, experimental verification is still a necessity.

Northern blotting is

frequently performed to investigate the expression of the

computationally predicted

candidates, or the predicted sequences are amplified from small

RNA libraries.

These procedures are not particularly compatible with

high-throughput screening,

and since many computational methods produce large numbers of

candidates, only a

small contingent of the predicti*** is usually subjected to

experimental verification,

whereas the majority remains untested. More importantly, the

experimental validation

methods are subject to many of the same limitati*** that hamper

the cloning

approach. Thus, even if an experimental verification is

attempted and fails, it is often

impossible to decide whether the failure was due to a

false-positive prediction, insufficient

sensitivity of the experimental detection method, or lack of

expression in the

tested tissue or cell line.

It is thus perhaps not surprising that the expression criterion

has not been satisfied

for most computationally predicted miRNA candidates. While some

groups have

attempted to reconcile these difficulties by developing

expression ***ysis tools that

are, for example, more sensitive or allow high-throughput

screening, there is also

tremendous effort to increase the reliability of computational

prediction methods

such that experimental confirmation is becoming less

important.

1.4 Computational Prediction of miRNA s

A plethora of computational approaches aimed at the prediction

of miRNAs have

been devised, and although nearly all of them use the evaluation

of features that are

thought to be characteristic for miRNAs in order to identify

novel candidates, they

vary significantly in scope, complexity, and level of

sophistication of the underlying

algorithms. Some approaches strive to identify the totality of

miRNAs encoded by a

given ***anism, whereas others aim to identify only miRNAs that

represent closely

related ortho- or paralogs of those that are already known. Some

programs investigate

some of the largest genomes, those of mammals, whereas others

c***ider only

some of the smallest, those of viruses.

Despite these differences, most of the approaches function

according to a common

scheme that might be abstracted as follows. First, a pool of

input sequences

(usually representing the complete genome of a given ***anism)

is filtered in order to

limit the number of candidates that have to be evaluated by

downstream algorithms.

I will refer to this process as upstream filtering in the

following. The filtered pool is

then subjected to a structure prediction. The obtained

structures are then compared

to those of known pre-miRNAs, and a score calculation is

performed, depending on

the degree of similarity. Finally, experimental validation is

attempted, usually for a

selection of the highest-scoring candidates.

There are c***iderable differences in the degree to which

structural features are

investigated during the scoring step; sometimes the filter might

simply ensure that the

candidate forms a hairpin structure, whereas in other cases it

might investigate the

candidate’s structure down to the minutest detail. The level of

sophistication, in large

part, will depend on the design of the upstream filter and the

efficiency with which

this filter preselects a set of candidates enriched for genuine

miRNAs. For example,

phylogenetic c***ervation is the most widely used upstream

filter (and at least presently,

it is also appears to be the most efficient). Indeed, if the

sequence of a known

mature miRNA is perfectly c***erved in a closely (or even

distantly) related species,

a relatively *** structural ***ysis that shows that the

ability of the surrounding

sequences to adopt a fold-back structure is c***erved as well

might suffice.

In contrast, an ab initio prediction method in which the

upstream filter is minimal

will require a much more detailed structural ***ysis during the

downstream scoring

step. Thus, a highly efficient upstream filter requires a less

elaborate downstream

structure evaluation, and vice versa. The cloning method might

be c***idered a special

case of this scheme in which the upstream filtering is based on

an experimental

procedure; since this method produces only little background,

the subsequent structural

investigation can be minimal.

All of the available computational approaches are subject to the

production

of false-positive (i.e., candidates that pass the filters but do

not represent genuine

miRNAs) and false-negative predicti*** (i.e., bona fide miRNAs

that are rejected

during the upstream filtering or the downstream scoring step).

The ratio with which

true-positive versus false-positive predicti*** are made will

determine the algorithm’s

accuracy, while the ratio of true-positive versus false-negative

predicti***

will determine its sensitivity. Such rates are frequently

estimated in order to judge an

algorithm’s performance.

Estimating the rate of false-negative predicti*** is a

relatively straightforward

process. Often, only a limited number of the contingent of known

miRNAs is used to

establish the parameters of the filtering and scoring

algorithms. The remaining miRNAs

are then subjected to the prediction procedure, and the number

of rejected versus

retained miRNAs is determined. Alternatively, the full

complement of miRNAs

is repeatedly passed through the filters, and the method

parameters are adjusted

until an acceptable ratio between rejected and accepted miRNAs

is achieved (what

exactly an acceptable ratio is will greatly vary with the

overall design and scope of

the method).

The estimation of false-positive prediction rates is a more

complicated matter:

in order to measure such numbers with high reliability, one

ideally would

have a set of sequences that assuredly does not contain any

miRNAs at all, or a

set in which all of the genuine miRNAs are known beforehand. In

theory, such

a set can be created artificially from randomly generated

sequences, or by shuffling

naturally occurring ones, but since biological sequences are

nonrandom, such

a reference set would be hardly representative of the

experimental sequence set.

Alternatively, one might select genetic elements that have known

functi*** and

are thus unlikely to additionally represent miRNAs, but this

would reduce the

complexity of the reference set so drastically that the gained

information would be

close to meaningless.

In reality, the rate of false-positive predicti*** is often

estimated on an experimental

basis. For this purpose, a representative subset of the

predicti*** (or all of

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